WhatsApp is working on a feature to let users hide certain groups in community chats., Technology &amp; Science News - Times Now

WhatsApp Users Will Soon Be Able To Hide Community Group Chats, Here Is How | Times Now

WhatsApp is testing a new feature that lets users quickly reply and react to images and photos without having to long press on them. Here&#x27;s how it works.

WhatsApp testing new feature that makes it easy to react to images and videos | Technology News - The Indian Express

Some beta testers can now explore a revamped interface for the status updates tray accessible through Google Play Store. | OpIndia News

WhatsApp adds interesting feature for its users: Know what it is

Meta-owned WhatsApp has said it banned more than 79 lakh accounts in India in the month of March, in compliance with the IT (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021.

WhatsApp Says Banned Over 79 Lakh Accounts In India In March

Washington [US], May 3 (ANI): In a bid to streamline communication and enhance group interactions, WhatsApp has introduced a new feature aimed at simplifying event organization within its platform. As per GSM Arena, the messaging giant announced the rollout of a novel tool designed to facilitate ...

WhatsApp introduces event notifications for group chats

WhatsApp banned 79.54 lakh accounts in March, proactively banning 14.30 lakh, as part of monthly report.

WhatsApp bans 79.54 lakh accounts in March - The Hindu BusinessLine

WhatsApp recently unveiled chat filters to help users easily organise their conversation and now the Meta-owned app is introducing some changes in this feature.Chats on WhatsApp were split into All,...

WhatsApp rolling out new changes in Chat Filters | Sci-Tech | thenews.com.pk

WhatsApp is launching a new feature to help users manage phone storage, including chat filtering for storage optimization. The tool allows users to prevent accidental deletion by filtering content types, enhancing data organization.

WhatsApp to roll out new feature to help manage phone storage: Report - The Economic Times

WhatsApp identifies Indian users by their phone numbers starting with +91. whatsapp, whatsapp accounts ban, whatsapp india report, whatsapp in india, whatsapp news, Technology &amp; Science News - Times Now

WhatsApp: Over 79 Lakh Indian Accounts Banned In March 2024, Here Is Why | Times Now

The feature is currently available to some beta testers who install the latest updates of WhatsApp beta for Android from the Google Play Store. whatsapp storage, whatsapp chat filter, whatsapp storage manage, whatsapp news, whatsapp update, whatsapp latest news, whatsapp latest update, Technology ...

WhatsApp's New Chat Filtering Feature Will Allow Users To Manage Storage, Here Is How | Times Now

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What Does It Take to Make AI Watch, Listen, and Understand Videos Like Us?

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance Abstract Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. 1. Introduction Recent efforts on Large Multimodal Models (LMMs), spearheaded by GPT-4V [25], allow detailed conversations about images but generally do not scale well to videos. The magnitude of video data scales far beyond other modalities due to its massive volume on social and internet media. Furthermore, extending LMMs to videos is challenging due to their complex dynamics with long temporal context that needs to be understood accurately. Although recent approaches towards video-LMMs such as VideoChat [15], Video-LLaMA [45], and Video-ChatGPT [22] have demonstrated capabilities in video comprehension and dialogue, they lack the crucial feature of visual grounding. Visual grounding in videos aims to associate the LMM responses to specific objects within the video input. Addressing this gap, we introduce PG-Video-LLaVA, the first video-LMM capable of localizing objects appearing in LMM responses. This task leads to enhanced intractability and demonstrates deep understanding of video content. In PG-Video-LLaVA, we address the unique challenges posed by video data. The model is designed to track objects within shorter video clips that maintain consistent camera views, enabling accurate visual grounding across scenes and motions. This tracking links spatio-temporal segments directly to conversational elements, enhancing the model’s contextual understanding. A salient feature of PG-VideoLLaVA is its modular design, allowing for easy integration with existing grounding modules and the flexibility to adapt to future enhancements in visual grounding technology. Moreover, PG-Video-LLaVA enriches its capabilities by incorporating audio context. It achieves this by leveraging video audio in a form understandable to LLM, which is particularly useful in situations where the auditory information is essential to the conversation. This inclusion broadens the model’s understanding, making it more versatile in interpreting video content. Furthermore, this work introduces an improved framework for benchmarking video-based conversational models, pivoting from previous approaches [22] that predominantly used the proprietary GPT-3.5-Turbo model for evaluation. Given that GPT-3.5-Turbo is subject to changes at any time and lacks transparency due to its closed-source nature, it presents challenges in terms of reliability and reproducibility. To address this, we propose the use of Vicuna, an open-source LLM for benchmarking. This shift not only enhances reproducibility but also improves transparency in the evaluation process. We evaluate PG-Video-LLaVA using our improved benchmarks and show notable improvements over existing video conversational models like VideoChatGPT [22] and Video-LLaMA [45] in ungrounded dialogues, achieving state-of-the-art (SoTA) performance. The key contributions of this work are: • We propose PG-Video-LLaVA, the first video-based LMM with pixel-level grounding capabilities, featuring a modular design for enhanced flexibility. • By incorporating audio context, PG-Video-LLaVA significantly enhances its understanding of video content, making it more comprehensive and aptly suited for scenarios where the audio signal is crucial for video understanding (e.g., dialogues and conversations, news videos, etc.). • We introduce improved quantitative benchmarks for video-based conversational models. Our benchmarks utilize open-source Vicuna LLM to ensure better reproducibility and transparency. We also propose benchmarks to evaluate the grounding capabilities of video-based conversational models. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 1 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance Abstract Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixellevel grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in VideoChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. 1. Introduction Recent efforts on Large Multimodal Models (LMMs), spearheaded by GPT-4V [25], allow detailed conversations about images but generally do not scale well to videos. The magnitude of video data scales far beyond other modalities due to its massive volume on social and internet media. Furthermore, extending LMMs to videos is challenging due to their complex dynamics with long temporal context that needs to be understood accurately. Although recent approaches towards video-LMMs such as VideoChat [15], Video-LLaMA [45], and Video-ChatGPT [22] have demonstrated capabilities in video comprehension and dialogue, they lack the crucial feature of visual grounding. Visual grounding in videos aims to associate the LMM responses to specific objects within the video input. Addressing this gap, we introduce PG-Video-LLaVA, the first video-LMM capable of localizing objects appearing in LMM responses. This task leads to enhanced intractability and demonstrates deep understanding of video content. In PG-Video-LLaVA, we address the unique challenges posed by video data. The model is designed to track objects within shorter video clips that maintain consistent camera views, enabling accurate visual grounding across scenes and motions. This tracking links spatio-temporal segments directly to conversational elements, enhancing the model’s contextual understanding. A salient feature of PG-VideoLLaVA is its modular design, allowing for easy integration with existing grounding modules and the flexibility to adapt to future enhancements in visual grounding technology. Moreover, PG-Video-LLaVA enriches its capabilities by incorporating audio context. It achieves this by leveraging video audio in a form understandable to LLM, which is particularly useful in situations where the auditory information is essential to the conversation. This inclusion broadens the model’s understanding, making it more versatile in interpreting video content. Furthermore, this work introduces an improved framework for benchmarking video-based conversational models, pivoting from previous approaches [22] that predominantly used the proprietary GPT-3.5-Turbo model for evaluation. Given that GPT-3.5-Turbo is subject to changes at any time and lacks transparency due to its closed-source nature, it presents challenges in terms of reliability and reproducibility. To address this, we propose the use of Vicuna, an open-source LLM for benchmarking. This shift not only enhances reproducibility but also improves transparency in the evaluation process. We evaluate PG-Video-LLaVA using our improved benchmarks and show notable improvements over existing video conversational models like VideoChatGPT [22] and Video-LLaMA [45] in ungrounded dialogues, achieving state-of-the-art (SoTA) performance. The key contributions of this work are: • We propose PG-Video-LLaVA, the first video-based LMM with pixel-level grounding capabilities, featuring a modular design for enhanced flexibility. • By incorporating audio context, PG-Video-LLaVA significantly enhances its understanding of video content, making it more comprehensive and aptly suited for scenarios where the audio signal is crucial for video understanding (e.g., dialogues and conversations, news videos, etc.). • We introduce improved quantitative benchmarks for video-based conversational models. Our benchmarks utilize open-source Vicuna LLM to ensure better reproducibility and transparency. We also propose benchmarks to evaluate the grounding capabilities of video-based conversational models. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

UAE Researchers Say New AI Model Can Watch Videos, Understand Audio

BƏƏ tədqiqatçıları deyirlər ki, yeni süni intellekt modeli videolara baxa, səsi anlaya bilər

Investigadores de los Emiratos Árabes Unidos afirman que un nuevo modelo de inteligencia artificial puede ver vídeos y comprender audio

UAEko ikertzaileek diote AI eredu berriak bideoak ikusi eta audioa ulertu ditzakeela

UAEの研究者らは、新しいAIモデルは動画を視聴し、音声を理解できると述べている

Pesquisadores dos Emirados Árabes Unidos dizem que novo modelo de IA pode assistir a vídeos e entender áudio

Emiratos Árabes Unidos Yachaysapakuna ninku Musuq AI Modelo Videokunata qhawayta atin, Audiota entiendeyta atin

Abashakashatsi ba UAE bavuga ko Moderi nshya ya AI ishobora kureba amashusho, gusobanukirwa amajwi

Vatsvagiri veUAE Vanoti New AI Model Inogona Kuona Vhidhiyo, Nzwisisa Audio

Sinasabi ng Mga Mananaliksik ng UAE na Makakapanood ng Mga Video, Makakaintindi ng Audio ang Bagong Modelo ng AI

متحدہ عرب امارات کے محققین کا کہنا ہے کہ نیا اے آئی ماڈل ویڈیوز دیکھ سکتا ہے، آڈیو کو سمجھ سکتا ہے۔

BAA tadqiqotchilari yangi sun'iy intellekt modeli videolarni ko'rishi, audioni tushunishi mumkinligini aytishdi

Abacwaningi be-UAE Bathi Imodeli Entsha ye-AI Ingabuka Amavidiyo, Iqonde Umsindo

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 5 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 4.1. Implementation Details For audio transcript extraction, base Whisper model is used. Our grounding module is based on GroundingDINOT variant and CLIP ViT-B/32. For the image-tagging model we use RAM Swin-Large variant (with input size 384). DEVA Tracker is applied under online-setting in our experiments. Vicuna-13b-v1.5 model is used in performing videobased conversational benchmarking, zero-shot question answering evaluation, and extracting the key noun or referring expression from the model output in the quantitative evaluation of the spatial grounding task. Further, Vicuna-13b-v1.5 was used to implement the entity matching as in [49]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 5 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 5 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 5 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 5 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 4.1. Implementation Details For audio transcript extraction, base Whisper model is used. Our grounding module is based on GroundingDINOT variant and CLIP ViT-B/32. For the image-tagging model we use RAM Swin-Large variant (with input size 384). DEVA Tracker is applied under online-setting in our experiments. Vicuna-13b-v1.5 model is used in performing videobased conversational benchmarking, zero-shot question answering evaluation, and extracting the key noun or referring expression from the model output in the quantitative evaluation of the spatial grounding task. Further, Vicuna-13b-v1.5 was used to implement the entity matching as in [49]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

UAE Researchers Teach AI to Watch, Listen, and Understand Videos Like Humans

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 6 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 4.2. Stronger Baseline This section provides an overview of the quantitative evaluations conducted to determine the effects of the strengthened baseline on PG-Video-LLaVA. To evaluate the impact of the enhanced baseline on PG-Video-LLaVA, we apply the benchmarking framework from Video-ChatGPT[22]. This framework measures performance on several axes critical for video-based conversational agents, including correctness of information, detail orientation, contextual understanding, temporal understanding, and consistency. In order to facilitate a reliable and reproducible evaluation, we have updated our assessment pipeline by replacing GPT-3.5-Turbo with Vicuna-13b-v1.5. This adjustment addresses the limitations in reproducibility inherent to the closed-source nature of GPT-3.5-Turbo. Subsequently, we have re-assessed both PG-Video-LLaVA and other recent models to ensure a fair and consistent comparison. The results shown in Table 1 demonstrate that PG-Video-LLaVA outperforms the foundational Video-ChatGPT model and exhibits superior performance when compared to other recent contributions in the domain. Following the quantitative assessment, the qualitative results in Figure 3 indicate the enhanced baseline’s impact on PG-Video-LLaVA’s performance. The PG-Video-LLaVA (13B) model exhibits improved accuracy in the information presented, a deeper level of descriptive detail, and a stronger alignment with the context and temporal progression of the videos. This advancement is particularly noticeable in the precise depiction of the child’s engagement with their surroundings and the giraffe’s behaviour, indicating a refined interpretation of both the activities and their settings. These qualitative insights are consistent with the quantitative results, highlighting the augmented baseline’s role in advancing PG-Video-LLaVA’s capacity in video understanding. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

AI Just Got Better at Watching Videos

Authors:
(1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution;
(2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution;
(3) Muhammad Maaz, Mohamed bin Zayed University of AI;
(4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI;
(5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University;
(6) Mubarak Shah, University of Central Florida;
(7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 3 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction
2. Related Works


PG-Video-LLaVA


3.1. Overview
3.2. Architecture


Experiments


4.1. Implementation Details
4.2. Stronger Baseline
4.3. Spatial Grounding in Videos
4.4. Zero-Shot Visual Question Answering
5. Conclusion and References Supplementary Material A. Audio Modality Integration
B. Visual Grounding: Quantitative Evaluation
C. Qualitative Results for Visual Grounding
D. Quantitative Evaluations of Video-based Conversation Performance 3.1. Overview In this paper, we introduce PG-Video-LLaVA, a novel Large Multimodal Model (LMM) designed to align video and audio representations with a Large Language Model (LLM). This integration equips PG-Video-LLaVA with the capability to proficiently manage both video and audio data in conversational contexts. Additionally, our method integrates a specialized plug-and-play module for effective video grounding (see Figure 2). In constructing PG-Video-LLaVA, our approach integrates sophisticated mechanisms for aligning video and audio signals with language processing capabilities, thereby facilitating a comprehensive multimodal analysis. Central to our model is an advanced CLIP-based video encoder, which has been specifically adapted to process both spatial and temporal dimensions of video data. This adaptation enables a deeper understanding of video content, setting PGVideo-LLaVA apart from conventional image-centric models. For training, PG-Video-LLaVA utilizes the VideoInstruct100K [22] dataset comprising 100K video instructions derived from ActivityNet-200 [11]. This diverse dataset ensures that the model is well-equipped to handle a broad spectrum of video contexts with high accuracy. In addition to visual processing, PG-Video-LLaVA incorporates stateof-the-art audio analysis by leveraging advanced audio transcription techniques, similar to those employed in WhisperX [2] and Whisper-AT[10]. This integration allows the model to process and understand audio inputs effectively, enhancing its overall multimodal interpretation capabilities. While PG-Video-LLaVA’s foundation is based on the LLaVA-1.5 [18] framework, it is extended for videos to incorporate spatio-temporal representations, audio understanding and visual grounding capabilities. Its unique combination of enhanced video encoding, extensive training dataset, integrated audio processing and grounding capability marks it as a forward step in the field of LMMs. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Authors: Authors: (1) Shehan Munasinghe, Mohamed bin Zayed University of AI and Equal Contribution; (2) Rusiru Thushara, Mohamed bin Zayed University of AI and Equal Contribution; (3) Muhammad Maaz, Mohamed bin Zayed University of AI; (4) Hanoona Abdul Rasheed, Mohamed bin Zayed University of AI; (5) Salman Khan, Mohamed bin Zayed University of AI and Australian National University; (6) Mubarak Shah, University of Central Florida; (7) Fahad Khan, Mohamed bin Zayed University of AI and Linköping University. Editor's Note: This is Part 3 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Editor's Note: This is Part 3 of 10 of a study detailing the development of a smarter AI model for videos. Read the rest below. Table of Links Abstract and 1 Introduction 2. Related Works PG-Video-LLaVA 3.1. Overview 3.2. Architecture Experiments 4.1. Implementation Details 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Works 2. Related Works PG-Video-LLaVA PG-Video-LLaVA PG-Video-LLaVA 3.1. Overview 3.1. Overview 3.2. Architecture 3.2. Architecture Experiments Experiments Experiments 4.1. Implementation Details 4.1. Implementation Details 4.2. Stronger Baseline 4.2. Stronger Baseline 4.3. Spatial Grounding in Videos 4.3. Spatial Grounding in Videos 4.4. Zero-Shot Visual Question Answering 4.4. Zero-Shot Visual Question Answering 5. Conclusion and References 5. Conclusion and References Supplementary Material Supplementary Material A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance A. Audio Modality Integration A. Audio Modality Integration B. Visual Grounding: Quantitative Evaluation B. Visual Grounding: Quantitative Evaluation C. Qualitative Results for Visual Grounding C. Qualitative Results for Visual Grounding D. Quantitative Evaluations of Video-based Conversation Performance D. Quantitative Evaluations of Video-based Conversation Performance 3.1. Overview In this paper, we introduce PG-Video-LLaVA, a novel Large Multimodal Model (LMM) designed to align video and audio representations with a Large Language Model (LLM). This integration equips PG-Video-LLaVA with the capability to proficiently manage both video and audio data in conversational contexts. Additionally, our method integrates a specialized plug-and-play module for effective video grounding (see Figure 2). In constructing PG-Video-LLaVA, our approach integrates sophisticated mechanisms for aligning video and audio signals with language processing capabilities, thereby facilitating a comprehensive multimodal analysis. Central to our model is an advanced CLIP-based video encoder, which has been specifically adapted to process both spatial and temporal dimensions of video data. This adaptation enables a deeper understanding of video content, setting PGVideo-LLaVA apart from conventional image-centric models. For training, PG-Video-LLaVA utilizes the VideoInstruct100K [22] dataset comprising 100K video instructions derived from ActivityNet-200 [11]. This diverse dataset ensures that the model is well-equipped to handle a broad spectrum of video contexts with high accuracy. In addition to visual processing, PG-Video-LLaVA incorporates stateof-the-art audio analysis by leveraging advanced audio transcription techniques, similar to those employed in WhisperX [2] and Whisper-AT[10]. This integration allows the model to process and understand audio inputs effectively, enhancing its overall multimodal interpretation capabilities. While PG-Video-LLaVA’s foundation is based on the LLaVA-1.5 [18] framework, it is extended for videos to incorporate spatio-temporal representations, audio understanding and visual grounding capabilities. Its unique combination of enhanced video encoding, extensive training dataset, integrated audio processing and grounding capability marks it as a forward step in the field of LMMs. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

This New AI Doesn’t Just Watch Videos—It Listens, Learns, and Talks Back Too

What to Watch for: 5 Must-Have Elements in Crypto Whitepapers

Kunas uñjañasa: 5 Elementos Must-Have en Cripto Janq’u qillqatanaka

За какво да внимавате: 5 задължителни елемента в крипто белите книги

Na šta treba obratiti pažnju: 5 obaveznih elemenata u kripto bijelim papirima

Worauf Sie achten sollten: 5 unverzichtbare Elemente in Krypto-Whitepapers

Qué tener en cuenta: 5 elementos imprescindibles en los documentos técnicos sobre criptomonedas

Zer ikusi: 5 elementu ezinbestekoak Crypto Whitepapers

O que hai que ter en conta: 5 elementos imprescindibles nos documentos brancos de cripto

למה לצפות: 5 אלמנטים חובה במסמכי קריפטו

注目すべき点: 暗号通貨のホワイトペーパーに必須の 5 つの要素

Kam jāpievērš uzmanība: 5 obligātie elementi kriptogrāfijās

สิ่งที่ต้องจับตามอง: 5 องค์ประกอบที่ต้องมีใน Crypto Whitepapers

Nämä tomaşa etmeli: Kripto sahypalarynda 5 element bolmaly

When considering whether to stay at your current company or move on, it's essential to recognize the red flags that indicate a problematic work environment. These warning signs can significantly impact your career growth, mental health, and overall job satisfaction. Perpetually Burning Deadlines One of the most significant red flags is consistently tight deadlines, where projects are always 'on fire'. The constant pressure to deliver projects quickly leaves little time for planning, thoughtful design, code review, and writing quality code. This environment often leads to poor planning, degraded code quality, stifled innovation, and high levels of stress. In the end, the blame is shifted to developers rather than the management, who fail to fulfill their responsibilities. Ignoring Problems and Solutions If a company ignores problems and dismisses suggestions for improvement, it definitely leads to stagnation and inefficiency. This frustrates specialists who want to contribute and solve issues, resulting in a lack of innovation, poor communication, and a sense of apathy throughout the company. As a result, talented employees feel undervalued and typically leave the company. Unrealistic Deadlines Another significant red flag is when management sets unrealistic deadlines without consulting the specialist team. This leads to improper planning, increased pressure at work, compromised quality, blame-shifting, and decreased team morale, similar to what happens with constantly burning deadlines. In such conditions, developers often suffer from severe stress and burnout, prompting them to seek a better working environment. Dishonesty For example, when it comes to salary increases, promotions, or any other promised benefits, and the employer does not fulfill those promises, it's a serious reason to consider changing company. Lying lowers morale and hinders professional growth, creating a culture of dishonesty. If a company has lied to its employees once, it will likely do so again. Therefore, it's important to raise an alarm and seriously think about finding a new job. There is no place for dishonesty in professional activities, whether it comes from management, developers, or the company as a whole - it doesn't matter. Manipulation and Toxicity Psychological manipulation and toxicity in the workplace greatly affect mental health and career growth. Emotional manipulation, raising voices, playing on ego, a blame culture, and poor leadership create a hostile environment. It can also suppress ambitions and the desire to develop, to the point where you may become tired not just of working in the company but of working and growing in the IT field altogether. High Employee Turnover High employee turnover is another significant red flag. If you start noticing that most of your colleagues have been with the company for less than, say, six months, there is likely a serious underlying issue. High turnover often indicates problems within the company, whether it's poor management, unrealistic deadlines, a toxic work atmosphere, or something else. High turnover is at least a reason to consider the potential problems that may only become apparent over time. Of course, these are not all the red flags that may indicate issues within a company. If you recognize your company in these signals, my advice is to consider changing jobs. If you are part of the company's management, pay attention to what not to do and what to work on to create an excellent working environment. When considering whether to stay at your current company or move on, it's essential to recognize the red flags that indicate a problematic work environment. These warning signs can significantly impact your career growth, mental health, and overall job satisfaction. Perpetually Burning Deadlines One of the most significant red flags is consistently tight deadlines, where projects are always 'on fire'. The constant pressure to deliver projects quickly leaves little time for planning, thoughtful design, code review, and writing quality code. This environment often leads to poor planning, degraded code quality, stifled innovation, and high levels of stress. In the end, the blame is shifted to developers rather than the management, who fail to fulfill their responsibilities. tight deadlines, where projects are always 'on fire'. Ignoring Problems and Solutions If a company ignores problems and dismisses suggestions for improvement , it definitely leads to stagnation and inefficiency. This frustrates specialists who want to contribute and solve issues, resulting in a lack of innovation, poor communication, and a sense of apathy throughout the company. As a result, talented employees feel undervalued and typically leave the company. company ignores problems and dismisses suggestions for improvement Unrealistic Deadlines Another significant red flag is when management sets unrealistic deadlines without consulting the specialist team. This leads to improper planning, increased pressure at work, compromised quality, blame-shifting, and decreased team morale, similar to what happens with constantly burning deadlines. In such conditions, developers often suffer from severe stress and burnout, prompting them to seek a better working environment. management sets unrealistic deadlines without consulting the specialist team. Dishonesty For example, when it comes to salary increases, promotions, or any other promised benefits, and the employer does not fulfill those promises, it's a serious reason to consider changing company. Lying lowers morale and hinders professional growth, creating a culture of dishonesty. If a company has lied to its employees once, it will likely do so again. Therefore, it's important to raise an alarm and seriously think about finding a new job. If a company has lied to its employees once, it will likely do so again. There is no place for dishonesty in professional activities, whether it comes from management, developers, or the company as a whole - it doesn't matter. There is no place for dishonesty in professional activities, whether it comes from management, developers, or the company as a whole - it doesn't matter. Manipulation and Toxicity Psychological manipulation and toxicity in the workplace greatly affect mental health and career growth. Emotional manipulation, raising voices, playing on ego, a blame culture, and poor leadership create a hostile environment. It can also suppress ambitions and the desire to develop, to the point where you may become tired not just of working in the company but of working and growing in the IT field altogether. you may become tired not just of working in the company but of working and growing in the IT field altogether. High Employee Turnover High employee turnover is another significant red flag. If you start noticing that most of your colleagues have been with the company for less than, say, six months, there is likely a serious underlying issue. High turnover often indicates problems within the company, whether it's poor management, unrealistic deadlines, a toxic work atmosphere, or something else. High turnover is at least a reason to consider the potential problems that may only become apparent over time. Of course, these are not all the red flags that may indicate issues within a company. If you recognize your company in these signals, my advice is to consider changing jobs. my advice is to consider changing jobs. If you are part of the company's management, pay attention to what not to do and what to work on to create an excellent working environment. pay attention to what not to do and what to work on

The Top 6 Red Flags in Companies You Should Watch Out For🚩

🔥Watchers has been nominated in HackerNoon's annual Startups of The Year awards in London, UK🔥 Please vote for us here ⬇️ [https://hackernoon.com/startups/europe/europe-london-england-uk]
[https://hackernoon.com/startups/industry/social-media?stup=66f327b36ea4677e23e48baf] Read more about us below to understand why we deserve your vote. Meet Watchers Watchers offers a comprehensive community-building tools designed for content platforms. Our solutions include public and private chats, audio and video streams, engagement and copy widgets, and advanced AI moderation. These tools are perfect for any platform with active users and topics for discussion—whether it’s VOD, OTT, betting, trading, education, or any other service, website, or app. Why let your audience take the conversation to external social media when you can host it directly on your platform? Our tools can be integrated quickly, and they drive key our partners’ metrics. With our chats and widgets, clients see an impressive boost in user retention, revenue, and LTV. The evolution of Watchers We began three years ago with a B2C app, which allowed us to deeply understand user behaviour, needs, and patterns firsthand. This direct experience was invaluable. Then, we shifted our focus to B2B, targeting three key industries: video platforms, sports broadcasting, and iGaming. Additionally, we expanded into trading and crypto trading markets, where community-building is essential. We've also worked with HR Tech and EdTech companies. The demand for trusted communities is growing, as people seek meaningful connections beyond what traditional social networks can offer. This creates ample opportunities for ongoing development. Milestones and achievements Last year, we were selected as a promising AI startup for AWS's Aim High Accelerator program. This year, we were chosen for the Google Startup Cloud Program, further validating our innovation. Additionally, we won the SIGMA Startup Pitch competitions in both São Paulo and Budapest, demonstrating that our service is recognised and valued across diverse markets. We’re constantly evolving—focusing on improving AI moderation, clients’ security, and introducing new tools to enhance user engagement. Our drive for continuous improvement never stops. What Startups of The Year means to us It’s always important to inform potential partners and clients about the opportunities and benefits our tools offer. We see Hackernoon as an excellent platform to introduce our solutions to a new audience and share our vision. Conclusion We’re always happy to discuss specific platforms’ needs and explore how we can help boost your business, turning it into a beloved brand by meeting the unique needs of your users. Schedule a demo with us to learn more—there might just be a perfect match. Or just vote! Vote for us today! [Insert link to startup voting page for your city, parent industry, or sub industry]. 🔥Watchers has been nominated in HackerNoon's annual Startups of The Year awards in London, UK🔥 🔥Watchers has been nominated in HackerNoon's annual Startups of The Year awards in London, UK🔥 Please vote for us here ⬇️ Please vote for us here [https://hackernoon.com/startups/europe/europe-london-england-uk] [https://hackernoon.com/startups/industry/social-media?stup=66f327b36ea4677e23e48baf] [ https://hackernoon.com/startups/europe/europe-london-england-uk ] https://hackernoon.com/startups/europe/europe-london-england-uk [ https://hackernoon.com/startups/industry/social-media?stup=66f327b36ea4677e23e48baf ] https://hackernoon.com/startups/industry/social-media?stup=66f327b36ea4677e23e48baf Read more about us below to understand why we deserve your vote. Read more about us below to understand why we deserve your vote. Meet Watchers Watchers offers a comprehensive community-building tools designed for content platforms. Our solutions include public and private chats, audio and video streams, engagement and copy widgets, and advanced AI moderation. These tools are perfect for any platform with active users and topics for discussion—whether it’s VOD, OTT, betting, trading, education, or any other service, website, or app. Why let your audience take the conversation to external social media when you can host it directly on your platform? Our tools can be integrated quickly, and they drive key our partners’ metrics. With our chats and widgets, clients see an impressive boost in user retention, revenue, and LTV. Watchers The evolution of Watchers We began three years ago with a B2C app, which allowed us to deeply understand user behaviour, needs, and patterns firsthand. This direct experience was invaluable. Then, we shifted our focus to B2B, targeting three key industries: video platforms, sports broadcasting, and iGaming. Additionally, we expanded into trading and crypto trading markets, where community-building is essential. We've also worked with HR Tech and EdTech companies. The demand for trusted communities is growing, as people seek meaningful connections beyond what traditional social networks can offer. This creates ample opportunities for ongoing development. Milestones and achievements Last year, we were selected as a promising AI startup for AWS's Aim High Accelerator program. This year, we were chosen for the Google Startup Cloud Program , further validating our innovation. Additionally, we won the SIGMA Startup Pitch competitions in both São Paulo and Budapest, demonstrating that our service is recognised and valued across diverse markets. We’re constantly evolving—focusing on improving AI moderation, clients’ security, and introducing new tools to enhance user engagement. Our drive for continuous improvement never stops. Aim High Accelerator Google Startup Cloud Program SIGMA Startup Pitch What Startups of The Year means to us It’s always important to inform potential partners and clients about the opportunities and benefits our tools offer. We see Hackernoon as an excellent platform to introduce our solutions to a new audience and share our vision. Hackernoon Conclusion We’re always happy to discuss specific platforms’ needs and explore how we can help boost your business, turning it into a beloved brand by meeting the unique needs of your users. Schedule a demo with us to learn more—there might just be a perfect match. Or just vote! Vote for us today! [Insert link to startup voting page for your city, parent industry, or sub industry]. Vote for us today! [Insert link to startup voting page for your city, parent industry, or sub industry]. Vote for us today! [ Insert link to startup voting page for your city, parent industry, or sub industry] . Insert link to startup voting page for your city, parent industry, or sub industry]

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