Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Related Work
2.1. Multimodal Learning
2.2. Multiple Instance Learning


Methodology
3.1. Preliminaries and Notations
3.2. Relations between Attention-based VPG and MIL
3.3. MIVPG for Multiple Visual Inputs
3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios


Experiments and 4.1. General Setup
4.2. Scenario 1: Samples with Single Image
4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding
4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study


Conclusion and References Related Work
2.1. Multimodal Learning
2.2. Multiple Instance Learning Related Work 2.1. Multimodal Learning 2.1. Multimodal Learning 2.2. Multiple Instance Learning 2.2. Multiple Instance Learning Methodology
3.1. Preliminaries and Notations
3.2. Relations between Attention-based VPG and MIL
3.3. MIVPG for Multiple Visual Inputs
3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios Methodology 3.1. Preliminaries and Notations 3.1. Preliminaries and Notations 3.2. Relations between Attention-based VPG and MIL 3.2. Relations between Attention-based VPG and MIL 3.3. MIVPG for Multiple Visual Inputs 3.3. MIVPG for Multiple Visual Inputs 3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios 3.4. Unveiling Instance Correlation in MIVPG for Enhanced Multi-instance Scenarios Experiments and 4.1. General Setup
4.2. Scenario 1: Samples with Single Image
4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding
4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study Experiments and 4.1. General Setup Experiments and 4.1. General Setup 4.2. Scenario 1: Samples with Single Image 4.2. Scenario 1: Samples with Single Image 4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding 4.3. Scenario 2: Samples with Multiple Images, with Each Image as a General Embedding 4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study 4.4. Scenario 3: Samples with Multiple Images, with Each Image Having Multiple Patches to be Considered and 4.5. Case Study Conclusion and References Conclusion and References Conclusion and References Supplementary Material Supplementary Material A. Detailed Architecture of QFormer A. Detailed Architecture of QFormer B. Proof of Proposition B. Proof of Proposition C. More Experiments C. More Experiments 5. Conclusion In this paper, we introduce the MIVPG, a flexible, general and powerful component to fuse enriched visual representations with MLLMs, which achieves superior performance in diverse use cases. We demonstrate that QFormer is a limited variant of MIVPG, providing theoretical support for its efficacy. We believe the enriched visual signals and advanced MIL-based techniques will contribute to the future development of MLLMs. References References [1] Famke Aeffner, Mark D Zarella, Nathan Buchbinder, Marilyn M Bui, Matthew R Goodman, Douglas J Hartman, Giovanni M Lujan, Mariam A Molani, Anil V Parwani, Kate Lillard, et al. Introduction to digital image analysis in wholeslide imaging: a white paper from the digital pathology association. Journal of pathology informatics, 10(1):9, 2019. 1 [2] JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, A Mensch, K Millican, M Reynolds, and R Ring. Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35:23716–23736, 2022. 1, 2, 3, 4 [3] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425– 2433, 2015. 1 [4] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. 1 [5] Gabriele Campanella, Matthew G Hanna, Luke Geneslaw, Allen Miraflor, Vitor Werneck Krauss Silva, Klaus J Busam, Edi Brogi, Victor E Reuter, David S Klimstra, and Thomas J Fuchs. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine, 25(8):1301–1309, 2019. 2 [6] Marc-Andre Carbonneau, Veronika Cheplygina, Eric ´ Granger, and Ghyslain Gagnon. Multiple instance learning: A survey of problem characteristics and applications. Pattern Recognition, 77:329–353, 2018. 2 [7] Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F Yago Vicente, Thomas Dideriksen, Himanshu Arora, et al. Abo: Dataset and benchmarks for real-world 3d object understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21126– 21136, 2022. 1, 7, 2 [8] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 1 [9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 3 [10] Ji Feng and Zhi-Hua Zhou. Deep miml network. In Proceedings of the AAAI conference on artificial intelligence, 2017. 2 [11] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6904–6913, 2017. 1 [12] Sepp Hochreiter and Jurgen Schmidhuber. Long short-term ¨ memory. Neural computation, 9(8):1735–1780, 1997. 6 [13] MD Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, and Hamid Laga. A comprehensive survey of deep learning for image captioning. ACM Computing Surveys (CsUR), 51(6):1–36, 2019. 1 [14] Le Hou, Dimitris Samaras, Tahsin M Kurc, Yi Gao, James E Davis, and Joel H Saltz. Patch-based convolutional neural network for whole slide tissue image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2424–2433, 2016. 2 [15] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017. 6 [16] Maximilian Ilse, Jakub Tomczak, and Max Welling. Attention-based deep multiple instance learning. In International conference on machine learning, pages 2127–2136. PMLR, 2018. 1, 2, 3 [17] Fahdi Kanavati, Gouji Toyokawa, Seiya Momosaki, Michael Rambeau, Yuka Kozuma, Fumihiro Shoji, Koji Yamazaki, Sadanori Takeo, Osamu Iizuka, and Masayuki Tsuneki. Weakly-supervised learning for lung carcinoma classification using deep learning. Scientific reports, 10(1):9297, 2020. 2 [18] Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried. Grounding language models to images for multimodal generation. arXiv preprint arXiv:2301.13823, 2023. 2 [19] Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning, pages 3744–3753. PMLR, 2019. 3, 5, 1 [20] Bin Li, Yin Li, and Kevin W Eliceiri. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14318–14328, 2021. 1, 2 [21] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888– 12900. PMLR, 2022. 1 [22] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023. 1, 2, 5, 6, 7 [23] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C Lawrence ´ Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014. 6 [24] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. 1, 2 [25] Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. Hierarchical question-image co-attention for visual question answering. Advances in neural information processing systems, 29, 2016. 1 [26] Ming Y Lu, Drew FK Williamson, Tiffany Y Chen, Richard J Chen, Matteo Barbieri, and Faisal Mahmood. Data-efficient and weakly supervised computational pathology on wholeslide images. Nature biomedical engineering, 5(6):555–570, 2021. 2 [27] Tiange Luo, Chris Rockwell, Honglak Lee, and Justin Johnson. Scalable 3d captioning with pretrained models, 2023. 7 [28] Oded Maron and Tomas Lozano-P ´ erez. A framework for ´ multiple-instance learning. Advances in neural information processing systems, 10, 1997. 2 [29] OpenAI. Gpt-4 technical report. ArXiv, abs/2303.08774, 2023. 1 [30] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018. [31] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 1 [32] Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. Self-critical sequence training for image captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7008–7024, 2017. 1 [33] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626, 2017. 2 [34] Zhuchen Shao, Hao Bian, Yang Chen, Yifeng Wang, Jian Zhang, Xiangyang Ji, et al. Transmil: Transformer based correlated multiple instance learning for whole slide image classification. Advances in neural information processing systems, 34:2136–2147, 2021. 2, 4, 5 [35] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR, 2019. 6 [36] Masayuki Tsuneki and Fahdi Kanavati. Inference of captions from histopathological patches. In International Conference on Medical Imaging with Deep Learning, pages 1235–1250. PMLR, 2022. 1, 6, 2 [37] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3 [38] Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. Fvqa: Fact-based visual question answering. IEEE transactions on pattern analysis and machine intelligence, 40(10):2413–2427, 2017. 1 [39] Peiqi Wang, William M Wells, Seth Berkowitz, Steven Horng, and Polina Golland. Using multiple instance learning to build multimodal representations. In International Conference on Information Processing in Medical Imaging, pages 457–470. Springer, 2023. 2 [40] Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai, and Wenyu Liu. Revisiting multiple instance neural networks. Pattern Recognition, 74:15–24, 2018. 2 [41] Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, and Vikas Singh. Nystromformer: A nystrom-based algorithm for approximating self-attention. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 14138–14148, 2021. 5 [42] Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang. Vision-language pre-training with triple contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15671– 15680, 2022. 2 [43] Ting Yao, Yingwei Pan, Yehao Li, Zhaofan Qiu, and Tao Mei. Boosting image captioning with attributes. In Proceedings of the IEEE international conference on computer vision, pages 4894–4902, 2017. 1 [44] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4651–4659, 2016. 1 [45] Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. Deep sets. Advances in neural information processing systems, 30, 2017. 3 [46] Hongrun Zhang, Yanda Meng, Yitian Zhao, Yihong Qiao, Xiaoyun Yang, Sarah E Coupland, and Yalin Zheng. Dtfdmil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18802–18812, 2022. 1, 2 [47] Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022. 2 [48] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023. 1, 2 [49] Xinliang Zhu, Jiawen Yao, Feiyun Zhu, and Junzhou Huang. Wsisa: Making survival prediction from whole slide histopathological images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7234–7242, 2017. 1 Authors:
(1) Wenliang Zhong, The University of Texas at Arlington (wxz9204@mavs.uta.edu);
(2) Wenyi Wu, Amazon (wenyiwu@amazon.com);
(3) Qi Li, Amazon (qlimz@amazon.com);
(4) Rob Barton, Amazon (rab@amazon.com);
(5) Boxin Du, Amazon (boxin@amazon.com);
(6) Shioulin Sam, Amazon (shioulin@amazon.com);
(7) Karim Bouyarmane, Amazon (bouykari@amazon.com);
(8) Ismail Tutar, Amazon (ismailt@amazon.com);
(9) Junzhou Huang, The University of Texas at Arlington (jzhuang@uta.edu). Authors: Authors: (1) Wenliang Zhong, The University of Texas at Arlington (wxz9204@mavs.uta.edu); (2) Wenyi Wu, Amazon (wenyiwu@amazon.com); (3) Qi Li, Amazon (qlimz@amazon.com); (4) Rob Barton, Amazon (rab@amazon.com); (5) Boxin Du, Amazon (boxin@amazon.com); (6) Shioulin Sam, Amazon (shioulin@amazon.com); (7) Karim Bouyarmane, Amazon (bouykari@amazon.com); (8) Ismail Tutar, Amazon (ismailt@amazon.com); (9) Junzhou Huang, The University of Texas at Arlington (jzhuang@uta.edu). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Microsoft

Amazon

Future MLLMs: Contribution of MIL-Based Techniques and Enriched Visual Signals

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

100 Days of AI Day 2: Enhancing Prompt Engineering for ChatGPT

The Noonification: Exploring Tool-Integrated Reasoning: Innovating Math-Proficient LLMs (10/5/2023)

The Noonification: Simple Database Migration Scripts On Your CI/CD step (10/16/2023)

The Noonification: Breaking Axioms in Program Execution (11/5/2023)

The Noonification: The Easiest Way to Create Your First NPM Package (12/15/2023)

A Coarse-to-Fine Approach to Text-Guided 3D Localization Without Ground Truth

100 Days of AI Day 2: Enhancing Prompt Engineering for ChatGPT

The Noonification: Exploring Tool-Integrated Reasoning: Innovating Math-Proficient LLMs (10/5/2023)

The Noonification: Simple Database Migration Scripts On Your CI/CD step (10/16/2023)

The Noonification: Breaking Axioms in Program Execution (11/5/2023)

The Noonification: The Easiest Way to Create Your First NPM Package (12/15/2023)

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps