Vestico

vestico

Architectural Designs

A Short History of the World, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE AGE OF MAMMALS THE AGE OF MAMMALS The opening of the next great period in the life of the earth, the Cainozoic period, was a period of upheaval and extreme volcanic activity. Now it was that the vast masses of the Alps and Himalayas and the mountain backbone of the Rockies and Andes were thrust up, and that the rude outlines of our present oceans and continents appeared. The map of the world begins to display a first dim resemblance to the map of to-day. It is estimated now that between forty and eighty million years have elapsed from the beginnings of the Cainozoic period to the present time. At the outset of the Cainozoic period the climate of the world was austere. It grew generally warmer until a fresh phase of great abundance was reached, after which conditions grew hard again and the earth passed into a series of extremely cold cycles, the Glacial Ages, from which apparently it is now slowly emerging. But we do not know sufficient of the causes of climatic change at present to forecast the possible fluctuations of climatic conditions that lie before us. We may be moving towards increasing sunshine or lapsing towards another glacial age; volcanic activity and the upheaval of mountain masses may be increasing or diminishing; we do not know; we lack sufficient science. With the opening of this period the grasses appear; for the first time there is pasture in the world; and with the full development of the once obscure mammalian type, appear a number of interesting grazing animals and of carnivorous types which prey upon these. At first these early mammals seem to differ only in a few characters from the great herbivorous and carnivorous reptiles that ages before had flourished and then vanished from the earth. A careless observer might suppose that in this second long age of warmth and plenty that was now beginning, nature was merely repeating the first, with herbivorous and carnivorous mammals to parallel the herbivorous and carnivorous dinosaurs, with birds replacing pterodactyls and so on. But this would be an altogether superficial comparison. The variety of the universe is infinite and incessant; it progresses eternally; history never repeats itself and no parallels are precisely true. The differences between the life of the Cainozoic and Mesozoic periods are far profounder than the resemblances. A MAMMAL OF THE EARLY CAINOZOIC PERIOD The Titanotherum (Brontops) Robustum The most fundamental of all these differences lies in the mental life of the two periods. It arises essentially out of the continuing contact of parent and offspring which distinguishes mammalian and in a lesser degree bird life, from the life of the reptile. With very few exceptions the reptile abandons its egg to hatch alone. The young reptile has no knowledge whatever of its parent; its mental life, such as it is, begins and ends with its own experiences. It may tolerate the existence of its fellows but it has no communication with them; it never imitates, never learns from them, is incapable of concerted action with them. Its life is that of an isolated individual. But with the suckling and cherishing of young which was distinctive of the new mammalian and avian strains arose the possibility of learning by imitation, of communication, by warning cries and other concerted action, of mutual control and instruction. A teachable type of life had come into the world. The earliest mammals of the Cainozoic period are but little superior in brain size to the more active carnivorous dinosaurs, but as we read on through the record towards modern times we find, in every tribe and race of the mammalian animals, a steady universal increase in brain capacity. For instance we find at a comparatively early stage that rhinoceros-like beasts appear. There is a creature, the Titanotherium, which lived in the earliest division of this period. It was probably very like a modern rhinoceros in its habits and needs. But its brain capacity was not one tenth that of its living successor. The earlier mammals probably parted from their offspring as soon as suckling was over, but, once the capacity for mutual understanding has arisen, the advantages of continuing the association are very great; and we presently find a number of mammalian species displaying the beginnings of a true social life and keeping together in herds, packs and flocks, watching each other, imitating each other, taking warning from each other’s acts and cries. This is something that the world had not seen before among vertebrated animals. Reptiles and fish may no doubt be found in swarms and shoals; they have been hatched in quantities and similar conditions have kept them together, but in the case of the social and gregarious mammals the association arises not simply from a community of external forces, it is sustained by an inner impulse. They are not merely like one another and so found in the same places at the same times; they like one another and so they keep together. STENOMYLUS HITCHCOCKI—A GIRAFFE-CAMEL Nat. Hist. Mus. SKELETON OF PROTOHIPPUS VENTICOLUS--EARLY HORSE Nat. Hist. Mus. This difference between the reptile world and the world of our human minds is one our sympathies seem unable to pass. We cannot conceive in ourselves the swift uncomplicated urgency of a reptile’s instinctive motives, its appetites, fears and hates. We cannot understand them in their simplicity because all our motives are complicated; our’s are balances and resultants and not simple urgencies. But the mammals and birds have self-restraint and consideration for other individuals, a social appeal, a self- control that is, at its lower level, after our own fashion. We can in consequence establish relations with almost all sorts of them. When they suffer they utter cries and make movements that rouse our feelings. We can make understanding pets of them with a mutual recognition. They can be tamed to self-restraint towards us, domesticated and taught. COMPARATIVE SIZES OF BRAINS OF RHINOCEROS AND DINOCERAS Nat. Hist. Mus. That unusual growth of brain which is the central fact of Cainozoic times marks a new communication and interdependence of individuals. It foreshadows the development of human societies of which we shall soon be telling. As the Cainozoic period unrolled, the resemblance of its flora and fauna to the plants and animals that inhabit the world to-day increased. The big clumsy Uintatheres and Titanotheres, the Entelodonts and Hyracodons, big clumsy brutes like nothing living, disappeared. On the other hand a series of forms led up by steady degrees from grotesque and clumsy predecessors to the giraffes, camels, horses, elephants, deer, dogs and lions and tigers of the existing world. The evolution of the horse is particularly legible upon the geological record. We have a fairly complete series of forms from a small tapir-like ancestor in the early Cainozoic. Another line of development that has now been pieced together with some precision is that of the llamas and camels. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2011). A Short History of the World. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/11870/11870-h/11870-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

THE AGE OF MAMMALS

THE SPIRITED HONEYMOON

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 8. Annotation Details In total, we had 192 participants who annotated the videos in the VEATIC dataset. Eighty-four participants annotated video IDs 0-82. One hundred and eight participants annotated video IDs 83-123 prior to the planning of the VEATIC dataset. In particular, Fifty-one participants annotated video IDs 83-94, twenty-five participants annotated video IDs 95-97, and 32 participants annotated video IDs 98-123. Another novelty of the VEATIC dataset is that it contains videos with interacting characters and ratings for separate characters in the same video. These videos are those with video IDs 98-123. For each consecutive video pair, the video frames are exactly the same, but the continuous emotion ratings are annotated based on different selected characters. Figure 11 shows an example. In this study, we first propose this annotation process because it affords future algorithms a way to test whether models learn the emotion of the selected characters given the interactions between characters and the exact same context information. A good emotion recognition algorithm should deal with this complicated situation. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 8. Annotation Details In total, we had 192 participants who annotated the videos in the VEATIC dataset. Eighty-four participants annotated video IDs 0-82. One hundred and eight participants annotated video IDs 83-123 prior to the planning of the VEATIC dataset. In particular, Fifty-one participants annotated video IDs 83-94, twenty-five participants annotated video IDs 95-97, and 32 participants annotated video IDs 98-123. Another novelty of the VEATIC dataset is that it contains videos with interacting characters and ratings for separate characters in the same video. These videos are those with video IDs 98-123. For each consecutive video pair, the video frames are exactly the same, but the continuous emotion ratings are annotated based on different selected characters. Figure 11 shows an example. In this study, we first propose this annotation process because it affords future algorithms a way to test whether models learn the emotion of the selected characters given the interactions between characters and the exact same context information. A good emotion recognition algorithm should deal with this complicated situation. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Annotation Details

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: টীকা বিবরণ

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Annotationsdetails

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos de contexto: detalles de anotaciones

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : détails de l'annotation

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: एनोटेशन विवरण

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: 注釈の詳細

VEATIC: 비디오 기반 감정 및 상황 데이터 세트의 영향 추적: 주석 세부정보

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: detalhes da anotação

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: Подробности аннотации

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Duygu Takibi: Ek Açıklama Ayrıntıları

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong Bộ dữ liệu ngữ cảnh: Chi tiết chú thích

VEATIC：基于视频的情境数据集中的情感和影响跟踪：注释详细信息

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 10. Subject Agreement Across Videos A benefit of the VEATIC dataset is that it has multiple annotators for each video with the minimum number of annotators for any given video being 25 and the maximum being 73. Emotion perception is subjective and observers judgments can vary across multiple people. Many of the previously published emotion datasets have a very low number of annotators, often having only single digit (n < 10) number of annotators. Having so few annotators is problematic because of the increased variance across observers. To show this, we calculated how the average rating for each video in our dataset varied if we randomly sampled, with replacement, five versus all annotators. We repeated this process 1000 times for each video and calculated the standard deviation of the recalculated average rating. Figure 12a shows how the standard deviation of the consensus rating across videos varies if we use either five or all annotators for each video. This analysis shows that having more annotators leads to much smaller standard deviations in the consensus rating which can lead to more accurate representations of the ground truth emotion in the videos. Additionally, We investigated how observers’ responses varied across videos by calculating the standard deviation across observers for each video. Figure 12b shows the standard deviations across videos. We find that the standard deviations for both valence and arousal dimensions were small with valence having an average standard deviation of µ = 0.248 and a median of 0.222 and arousal having an average standard deviation of µ = 0.248 and a median of 0.244, which are comparable with the valence and arousal rating variance from EMOTIC [32]. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 10. Subject Agreement Across Videos A benefit of the VEATIC dataset is that it has multiple annotators for each video with the minimum number of annotators for any given video being 25 and the maximum being 73. Emotion perception is subjective and observers judgments can vary across multiple people. Many of the previously published emotion datasets have a very low number of annotators, often having only single digit (n < 10) number of annotators. Having so few annotators is problematic because of the increased variance across observers. To show this, we calculated how the average rating for each video in our dataset varied if we randomly sampled, with replacement, five versus all annotators. We repeated this process 1000 times for each video and calculated the standard deviation of the recalculated average rating. Figure 12a shows how the standard deviation of the consensus rating across videos varies if we use either five or all annotators for each video. This analysis shows that having more annotators leads to much smaller standard deviations in the consensus rating which can lead to more accurate representations of the ground truth emotion in the videos. Additionally, We investigated how observers’ responses varied across videos by calculating the standard deviation across observers for each video. Figure 12b shows the standard deviations across videos. We find that the standard deviations for both valence and arousal dimensions were small with valence having an average standard deviation of µ = 0.248 and a median of 0.222 and arousal having an average standard deviation of µ = 0.248 and a median of 0.244, which are comparable with the valence and arousal rating variance from EMOTIC [32]. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Subject Agreement Across Videos

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: ভিডিও জুড়ে বিষয় চুক্তি

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Subjektübereinstimmung über Videos hinweg

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: acuerdo de temas entre videos

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : accord de sujet dans les vidéos

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: वीडियो में विषय सहमति

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: ビデオ間の主題の一致

VEATIC: 비디오 기반 감정 및 맥락 데이터 세트의 영향 추적: 비디오 전반의 주제 일치

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: acordo de assunto entre vídeos

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: предметное соглашение по видео

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Duygu Takibi: Videolar Arasında Konu Anlaşması

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong Bộ dữ liệu ngữ cảnh: Thỏa thuận chủ đề trên các video

VEATIC：基于视频的情感和情境数据集跟踪：跨视频的主题一致性

Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 6. Conclusion In this study, we proposed the first context based large video dataset, VEATIC, for continuous valence and arousal prediction. Various visualizations show the diversity of our dataset and the consistency of our annotations. We also proposed a simple baseline algorithm to solve this challenge. Empirical results prove the effectiveness of our proposed method and the VEATIC dataset. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 6. Conclusion In this study, we proposed the first context based large video dataset, VEATIC , for continuous valence and arousal prediction. Various visualizations show the diversity of our dataset and the consistency of our annotations. We also proposed a simple baseline algorithm to solve this challenge. Empirical results prove the effectiveness of our proposed method and the VEATIC dataset. VEATIC This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Conclusion

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: উপসংহার

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Fazit

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: Conclusión

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : Conclusion

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: निष्कर्ष

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: 結論

VEATIC: 상황 데이터 세트의 비디오 기반 감정 및 영향 추적: 결론

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: Conclusão

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: заключение

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Etki Takibi: Sonuç

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong bộ dữ liệu ngữ cảnh: Kết luận

VEATIC：基于视频的情境数据集中的情感和影响跟踪：结论

Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 2. Related Work Recently, there have been several datasets that provide frames with both facial and context information, like CAER [33] and EMOTIC [32]. CAER [33] is a videobased dataset that contains categorical labels of each video frame, and EMOTIC [32] is an image-based dataset containing both categorical expression labels and continuous valence-arousal-dominance ratings. Unlike these datasets, our dataset is video-based and contains continuous valence and arousal ratings. A detailed comparison between our dataset with previous datasets can be found in Table 1. Based on various emotion datasets, studies have started to focus on how to infer emotion automatically. Human affect can be inferred from many modalities, such as audio [70, 68, 65], visual [40, 54, 55, 37], and text [68, 22]. For visual inputs, in particular, there are three major tasks. The valence-arousal estimation task aims to predict the valence and arousal of each image/frame [71, 69, 29, 30]; the expression recognition task focuses on classifying emotional categories of each image/frame [66, 57, 67]; and the action unit (AU) detection task intends to detect facial muscle actions from the faces of each image/frame [25, 56, 35, 64]. Currently, most proposed methods rely highly on the facial area to infer the emotional state. Indeed, the facial area contains rich information about the human emotional state. However, contextual factors also provide essential information that is necessary for humans to correctly infer and perceive the emotional states of others [8, 9, 10]. Several studies [33, 32, 40] have started to incorporate context information as a source of affect inference. In this study, we also adopted both facial and context information to achieve the new task, i.e., to infer the valence and arousal for each video frame. To infer the affect of a person, we usually need to deal with temporal information of either audio segments, video frames, or words. Many studies [68, 69, 29, 30] started to utilize long short term memory (LSTM) [23], gated recurrent unit (GRU) [11], or recurrent neural network (RNN) [24, 50] to process the temporal information. With the emergence of the visual transformer (ViT) [14], attention has been shifted. Many video understanding tasks [19, 1, 36] have utilized ViT for temporal information understanding and achieving state-of-the-art performance. Our baseline method also adopted ViT as a tool to process the temporal information in video clips. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 2. Related Work Recently, there have been several datasets that provide frames with both facial and context information, like CAER [33] and EMOTIC [32]. CAER [33] is a videobased dataset that contains categorical labels of each video frame, and EMOTIC [32] is an image-based dataset containing both categorical expression labels and continuous valence-arousal-dominance ratings. Unlike these datasets, our dataset is video-based and contains continuous valence and arousal ratings. A detailed comparison between our dataset with previous datasets can be found in Table 1. Based on various emotion datasets, studies have started to focus on how to infer emotion automatically. Human affect can be inferred from many modalities, such as audio [70, 68, 65], visual [40, 54, 55, 37], and text [68, 22]. For visual inputs, in particular, there are three major tasks. The valence-arousal estimation task aims to predict the valence and arousal of each image/frame [71, 69, 29, 30]; the expression recognition task focuses on classifying emotional categories of each image/frame [66, 57, 67]; and the action unit (AU) detection task intends to detect facial muscle actions from the faces of each image/frame [25, 56, 35, 64]. Currently, most proposed methods rely highly on the facial area to infer the emotional state. Indeed, the facial area contains rich information about the human emotional state. However, contextual factors also provide essential information that is necessary for humans to correctly infer and perceive the emotional states of others [8, 9, 10]. Several studies [33, 32, 40] have started to incorporate context information as a source of affect inference. In this study, we also adopted both facial and context information to achieve the new task, i.e., to infer the valence and arousal for each video frame. To infer the affect of a person, we usually need to deal with temporal information of either audio segments, video frames, or words. Many studies [68, 69, 29, 30] started to utilize long short term memory (LSTM) [23], gated recurrent unit (GRU) [11], or recurrent neural network (RNN) [24, 50] to process the temporal information. With the emergence of the visual transformer (ViT) [14], attention has been shifted. Many video understanding tasks [19, 1, 36] have utilized ViT for temporal information understanding and achieving state-of-the-art performance. Our baseline method also adopted ViT as a tool to process the temporal information in video clips. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Related Work

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: সম্পর্কিত কাজ

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Verwandte Arbeiten

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: trabajo relacionado

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : travaux connexes

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: संबंधित कार्य

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: 関連研究

VEATIC: 비디오 기반 감정 및 상황 데이터 세트의 영향 추적: 관련 작업

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: trabalho relacionado

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: соответствующая работа

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Etki Takibi: İlgili Çalışma

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong bộ dữ liệu ngữ cảnh: Công việc liên quan

VEATIC：基于视频的情境数据集中的情感和影响跟踪：相关工作

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 7. More About Stimuli All videos used in the VEATIC dataset were selected from an online video-sharing website (YouTube). The VEATIC dataset contains 124 video clips, 104 clips from Hollywood movies, 15 clips from home videos, and 5 clips from documentaries or reality TV shows. Specifically, we classify Documentary videos as any videos that show candid social interactions but have some form of video editing, while home videos refer to videos that show candid social interactions without any video editing. All Videos in the dataset had a frame rate of 25 frames per second and ranged in resolution with the lowest being 202 x 360 and the highest being 1920 x 1080. Except for the overview of video frames in Figure 2, we show more samples in Figure 9. Moreover, unlike previously published datasets where most frames contain the main character [31, 29, 32], VEATIC not only has frames containing the selected character but also there are lots of frames containing unselected characters and pure backgrounds (Figure 10). Therefore, VEATIC is more similar to our daily life scenarios, and the algorithms trained on it will be more promising for daily applications. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 7. More About Stimuli All videos used in the VEATIC dataset were selected from an online video-sharing website (YouTube). The VEATIC dataset contains 124 video clips, 104 clips from Hollywood movies, 15 clips from home videos, and 5 clips from documentaries or reality TV shows. Specifically, we classify Documentary videos as any videos that show candid social interactions but have some form of video editing, while home videos refer to videos that show candid social interactions without any video editing. All Videos in the dataset had a frame rate of 25 frames per second and ranged in resolution with the lowest being 202 x 360 and the highest being 1920 x 1080. Except for the overview of video frames in Figure 2, we show more samples in Figure 9. Moreover, unlike previously published datasets where most frames contain the main character [31, 29, 32], VEATIC not only has frames containing the selected character but also there are lots of frames containing unselected characters and pure backgrounds (Figure 10). Therefore, VEATIC is more similar to our daily life scenarios, and the algorithms trained on it will be more promising for daily applications. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: More About Stimuli

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: উদ্দীপনা সম্পর্কে আরও

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Mehr über Stimuli

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: más sobre estímulos

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : en savoir plus sur les stimuli

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: उत्तेजनाओं के बारे में अधिक जानकारी

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: 刺激についての詳細

VEATIC: 비디오 기반 감정 및 상황 데이터 세트의 영향 추적: 자극에 대한 추가 정보

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: mais sobre estímulos

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: больше о стимулах

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Etki Takibi: Uyaranlar Hakkında Daha Fazla Bilgi

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong bộ dữ liệu ngữ cảnh: Tìm hiểu thêm về kích thích

VEATIC：基于视频的情境数据集中的情感和影响跟踪：更多关于刺激的信息

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 4. Experiments In this study, we propose a new emotion recognition in context task, i.e. to infer the valence and arousal of the selected character via both context and character information in each video frame. Here, we propose a simple baseline model to benchmark the new emotion recognition in context task. The pipeline of the model is shown in Figure 8. We adopted two simple submodules: a convolutional neural network (CNN) module for feature extraction and a visual transformer module for temporal information processing. The CNN module structure is adopted from Resnet50 [21]. Unlike CAER [33] and EMOTIC [32], where facial/character and context features are extracted separately and merged later, we directly encode the fully informed frame. For a single prediction, consecutive N video frames are encoded independently. Then, the feature vectors of consecutive frames are first position embedded and fed into the transformer encoder containing L sets of attention modules. At last, the prediction of arousal and valence is accomplished by a multilayer perceptron (MLP) head. 4.1. Loss Function and Training Setup The loss function of our baseline model is a weighted combination of two separate losses. The MSE loss regularizes the local alignment of the ground truth of ratings and the model predictions. In order to guarantee the alignment of the ratings and predictions on a larger scale, such as learning the temporal statistics of the emotional ratings, we also utilize the concordance correlation coefficient (CCC) as a regularization. This coefficient is defined as follows, 4.2. Evaluation Metrics The SAGR measures how much the signs of the individual values of two vectors X and Y match. It takes on values in [0, 1], where 1 represents the complete agreement and 0 represents a complete contradiction. The SAGR metric can capture additional performance information than others. For example, given a valence ground truth of 0.2, predictions of 0.7 and -0.3 will lead to the same RMSE value. But clearly, 0.7 is better suited because it is a positive valence. 4.3. Benchmark Results We benchmark the new emotion recognition in context task using the aforementioned 4 metrics, CCC, PCC, RMSE, and SAGR. Results are shown in Table 3. Compared to other datasets, our proposed simple method is on par with state-of-the-art methods on their datasets. We also investigate the importance of context and character information in emotion recognition tasks by feeding the context-only and character-only frames into the pretrained model on fully-informed frames. In order to obtain fair comparisons and exclude the influence of frame pixel distribution differences, we also fine-tune the pretrained model on the context-only and character-only frames. The corresponding results are shown in Table 3 as well. Without full information, the model performances drop for both context-only and character-only conditions. In order to show the effectiveness of the VEATIC dataset, we utilized our pretrained model on VEATIC, finetuned it on other datasets, and tested its performance. We only tested for EMOTIC [32] and CAER-S [33] given the simplicity of our model and the similarity of our model to the models proposed in other dataset papers. The results are shown in Table 4. Our pretrained model performs on par with the proposed methods in EMOTIC [32] and CAERS [33]. Thus, it shows the effectiveness of our proposed VEATIC dataset. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 4. Experiments In this study, we propose a new emotion recognition in context task, i.e. to infer the valence and arousal of the selected character via both context and character information in each video frame. Here, we propose a simple baseline model to benchmark the new emotion recognition in context task. The pipeline of the model is shown in Figure 8. We adopted two simple submodules: a convolutional neural network (CNN) module for feature extraction and a visual transformer module for temporal information processing. The CNN module structure is adopted from Resnet50 [21]. Unlike CAER [33] and EMOTIC [32], where facial/character and context features are extracted separately and merged later, we directly encode the fully informed frame. For a single prediction, consecutive N video frames are encoded independently. Then, the feature vectors of consecutive frames are first position embedded and fed into the transformer encoder containing L sets of attention modules. At last, the prediction of arousal and valence is accomplished by a multilayer perceptron (MLP) head. 4.1. Loss Function and Training Setup The loss function of our baseline model is a weighted combination of two separate losses. The MSE loss regularizes the local alignment of the ground truth of ratings and the model predictions. In order to guarantee the alignment of the ratings and predictions on a larger scale, such as learning the temporal statistics of the emotional ratings, we also utilize the concordance correlation coefficient (CCC) as a regularization. This coefficient is defined as follows, 4.2. Evaluation Metrics The SAGR measures how much the signs of the individual values of two vectors X and Y match. It takes on values in [0, 1], where 1 represents the complete agreement and 0 represents a complete contradiction. The SAGR metric can capture additional performance information than others. For example, given a valence ground truth of 0.2, predictions of 0.7 and -0.3 will lead to the same RMSE value. But clearly, 0.7 is better suited because it is a positive valence. 4.3. Benchmark Results We benchmark the new emotion recognition in context task using the aforementioned 4 metrics, CCC, PCC, RMSE, and SAGR. Results are shown in Table 3. Compared to other datasets, our proposed simple method is on par with state-of-the-art methods on their datasets. We also investigate the importance of context and character information in emotion recognition tasks by feeding the context-only and character-only frames into the pretrained model on fully-informed frames. In order to obtain fair comparisons and exclude the influence of frame pixel distribution differences, we also fine-tune the pretrained model on the context-only and character-only frames. The corresponding results are shown in Table 3 as well. Without full information, the model performances drop for both context-only and character-only conditions. In order to show the effectiveness of the VEATIC dataset, we utilized our pretrained model on VEATIC, finetuned it on other datasets, and tested its performance. We only tested for EMOTIC [32] and CAER-S [33] given the simplicity of our model and the similarity of our model to the models proposed in other dataset papers. The results are shown in Table 4. Our pretrained model performs on par with the proposed methods in EMOTIC [32] and CAERS [33]. Thus, it shows the effectiveness of our proposed VEATIC dataset. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Experiments

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: পরীক্ষাগুলি

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Experimente

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: experimentos

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : Expériences

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: प्रयोग

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: 実験

VEATIC: 비디오 기반 감정 및 상황 데이터 세트의 영향 추적: 실험

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: experimentos

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: эксперименты

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Duygu Takibi: Deneyler

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong bộ dữ liệu ngữ cảnh: Thử nghiệm

VEATIC：基于视频的情境数据集中的情感和影响跟踪：实验

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu);
(4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu);
(5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu);
(7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References 5. Discussion Understanding how humans infer the emotions of others is essential for researchers understanding of social cognition. While psychophysicists conduct experiments, they need specific stimulus sets to design experiments. However, among published datasets, there is currently no contextbased video dataset that contains continuous valence and arousal ratings. The lack of this kind of datasets also prevents researchers from developing computer vision algorithms for the corresponding tasks. Our proposed VEATIC dataset fills in this important gap in the field of computer vision and will be beneficial for psychophysical studies in understanding emotion recognition. D During data collection, participants continuously tracked and rated the emotions of target characters in the video clips which is different from general psychophysical experiments where responses are collected after a delay. This design in our dataset was vital in order to mimic the real-time emotion processing that occurs when humans process emotions in their everyday lives. Additionally, emotion processing is not an immediate process and it relies heavily on the temporal accumulation of information over time in order to make accurate inferences about the emotions of others. The strength of the VEATIC dataset is that it mimics how humans perceive emotions in the real world: continuously and in the presence of contextual information both in the temporal and spatial domain. Such a rich dataset is vital for future computer vision models and can push the boundaries of what current models can accomplish. With the creation of more rich datasets like VEATIC, it may be possible for future computer vision models to perceive emotions in realtime while interacting with humans. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: peter.zhren@berkeley.edu); (2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: jefferson_ortega@berkeley.edu); (3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: wyf020803@berkeley.edu); (4) Zhimin Chen, University of California, Berkeley (Email: zhimin@berkeley.edu); (5) Yunhui Guo, University of Texas at Dallas (Email: yunhui.guo@utdallas.edu); (6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: stellayu@umich.edu); (7) David Whitney, University of California, Berkeley (Email: dwhitney@berkeley.edu). Table of Links Abstract and Intro Related Wok VEATIC Dataset Experiments Discussion Conclusion More About Stimuli Annotation Details Outlier Processing Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Abstract and Intro Abstract and Intro Related Wok Related Wok VEATIC Dataset VEATIC Dataset Experiments Experiments Discussion Discussion Conclusion Conclusion More About Stimuli More About Stimuli Annotation Details Annotation Details Outlier Processing Outlier Processing Subject Agreement Across Videos Subject Agreement Across Videos Familiarity and Enjoyment Ratings and References Familiarity and Enjoyment Ratings and References 5. Discussion Understanding how humans infer the emotions of others is essential for researchers understanding of social cognition. While psychophysicists conduct experiments, they need specific stimulus sets to design experiments. However, among published datasets, there is currently no contextbased video dataset that contains continuous valence and arousal ratings. The lack of this kind of datasets also prevents researchers from developing computer vision algorithms for the corresponding tasks. Our proposed VEATIC dataset fills in this important gap in the field of computer vision and will be beneficial for psychophysical studies in understanding emotion recognition. D During data collection, participants continuously tracked and rated the emotions of target characters in the video clips which is different from general psychophysical experiments where responses are collected after a delay. This design in our dataset was vital in order to mimic the real-time emotion processing that occurs when humans process emotions in their everyday lives. Additionally, emotion processing is not an immediate process and it relies heavily on the temporal accumulation of information over time in order to make accurate inferences about the emotions of others. The strength of the VEATIC dataset is that it mimics how humans perceive emotions in the real world: continuously and in the presence of contextual information both in the temporal and spatial domain. Such a rich dataset is vital for future computer vision models and can push the boundaries of what current models can accomplish. With the creation of more rich datasets like VEATIC, it may be possible for future computer vision models to perceive emotions in realtime while interacting with humans. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Discussion

VEATIC: ভিডিও-ভিত্তিক আবেগ এবং প্রসঙ্গ ডেটাসেটে ট্র্যাকিংকে প্রভাবিত করে: আলোচনা

VEATIC: Videobasiertes Emotions- und Affekt-Tracking im Kontextdatensatz: Diskussion

VEATIC: Seguimiento de emociones y afectos basado en videos en un conjunto de datos contextual: discusión

VEATIC : Suivi vidéo des émotions et des effets dans un ensemble de données contextuelles : Discussion

VEATIC: संदर्भ डेटासेट में वीडियो-आधारित भावना और प्रभाव ट्रैकिंग: चर्चा

VEATIC: コンテキストデータセットにおけるビデオベースの感情と感情の追跡: ディスカッション

VEATIC: 비디오 기반 감정 및 상황 데이터 세트의 영향 추적: 토론

VEATIC: Rastreamento de emoção e afeto baseado em vídeo em conjunto de dados de contexto: discussão

VEATIC: Отслеживание эмоций и аффектов на основе видео в наборе контекстных данных: обсуждение

VEATIC: Bağlam Veri Kümesinde Video Tabanlı Duygu ve Etki Takibi: Tartışma

VEATIC: Theo dõi cảm xúc và tác động dựa trên video trong bộ dữ liệu ngữ cảnh: Thảo luận

VEATIC：基于视频的情境数据集中的情感和影响跟踪：讨论

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