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TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering (2404.01476v2)

Published 1 Apr 2024 in cs.CV, cs.AI, cs.CL, and cs.LG

Abstract: Recently, image-based Large Multimodal Models (LMMs) have made significant progress in video question-answering (VideoQA) using a frame-wise approach by leveraging large-scale pretraining in a zero-shot manner. Nevertheless, these models need to be capable of finding relevant information, extracting it, and answering the question simultaneously. Currently, existing methods perform all of these steps in a single pass without being able to adapt if insufficient or incorrect information is collected. To overcome this, we introduce a modular multi-LMM agent framework based on several agents with different roles, instructed by a Planner agent that updates its instructions using shared feedback from the other agents. Specifically, we propose TraveLER, a method that can create a plan to "Traverse" through the video, ask questions about individual frames to "Locate" and store key information, and then "Evaluate" if there is enough information to answer the question. Finally, if there is not enough information, our method is able to "Replan" based on its collected knowledge. Through extensive experiments, we find that the proposed TraveLER approach improves performance on several VideoQA benchmarks without the need to fine-tune on specific datasets. Our code is available at https://github.com/traveler-framework/TraveLER.

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References (61)
  1. Flamingo: a visual language model for few-shot learning, 2022.
  2. Neural module networks, 2017.
  3. Bringing image scene structure to video via frame-clip consistency of object tokens. In Thirty-Sixth Conference on Neural Information Processing Systems, 2022.
  4. Memory consolidation enables long-context video understanding, 2024.
  5. Compositional video synthesis with action graphs. In ICML, 2021.
  6. Object level visual reasoning in videos. In European Conference on Computer Vision, pp.  105–121, 2018.
  7. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.
  8. Language models are few-shot learners, 2020.
  9. Revisiting the ”video” in video-language understanding, 2022.
  10. Video chatcaptioner: Towards enriched spatiotemporal descriptions, 2023a.
  11. Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset, 2023b.
  12. Zero-shot video question answering with procedural programs, 2023.
  13. Scaling instruction-finetuned language models, 2022.
  14. Violet : End-to-end video-language transformers with masked visual-token modeling, 2022.
  15. Recursive visual programming, 2023.
  16. Visual programming: Compositional visual reasoning without training, 2022.
  17. Mapping images to scene graphs with permutation-invariant structured prediction. In Advances in Neural Information Processing Systems (NIPS), 2018.
  18. Spatio-temporal action graph networks. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp.  0–0, 2019.
  19. Learning canonical representations for scene graph to image generation. In European Conference on Computer Vision, 2020.
  20. Object-region video transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  21. Incorporating structured representations into pretrained vision \& language models using scene graphs. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
  22. Promptonomyvit: Multi-task prompt learning improves video transformers using synthetic scene data. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.  6803–6815, January 2024.
  23. Learning object detection from captions via textual scene attributes. ArXiv, abs/2009.14558, 2020.
  24. Referring relationships. European Conference on Computer Vision, 2018.
  25. Qvhighlights: Detecting moments and highlights in videos via natural language queries, 2021a.
  26. Less is more: Clipbert for video-and-language learning via sparse sampling, 2021b.
  27. Lavender: Unifying video-language understanding as masked language modeling, 2022.
  28. Video-llava: Learning united visual representation by alignment before projection, 2023a.
  29. Mm-vid: Advancing video understanding with gpt-4v(ision), 2023b.
  30. Lgdn: Language-guided denoising network for video-language modeling, 2022.
  31. Video-chatgpt: Towards detailed video understanding via large vision and language models, 2023.
  32. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering, 2017.
  33. Compositional chain-of-thought prompting for large multimodal models. ArXiv, abs/2311.17076, 2023.
  34. Verbs in action: Improving verb understanding in video-language models, 2023.
  35. A simple recipe for contrastively pre-training video-first encoders beyond 16 frames, 2023.
  36. Perception test: A diagnostic benchmark for multimodal video models, 2023.
  37. Locate before answering: Answer guided question localization for video question answering, 2023.
  38. Modular visual question answering via code generation, 2023.
  39. Long-form video-language pre-training with multimodal temporal contrastive learning, 2023.
  40. Vipergpt: Visual inference via python execution for reasoning. Proceedings of IEEE International Conference on Computer Vision (ICCV), 2023.
  41. Internvideo: General video foundation models via generative and discriminative learning, 2022a.
  42. Language models with image descriptors are strong few-shot video-language learners. arXiv preprint arXiv:2205.10747, 2022b.
  43. Star: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS), 2021.
  44. Multi-agent reinforcement learning based frame sampling for effective untrimmed video recognition, 2019.
  45. Next-qa:next phase of question-answering to explaining temporal actions, 2021.
  46. Clip-vip: Adapting pre-trained image-text model to video-language representation alignment, 2023.
  47. Unifying the video and question attentions for open-ended video question answering. IEEE Transactions on Image Processing, 26(12):5656–5666, 2017. doi: 10.1109/TIP.2017.2746267.
  48. Zero-shot video question answering via frozen bidirectional language models, 2022.
  49. Vid2seq: Large-scale pretraining of a visual language model for dense video captioning, 2023.
  50. Hitea: Hierarchical temporal-aware video-language pre-training, 2022.
  51. Coca: Contrastive captioners are image-text foundation models, 2022.
  52. Self-chained image-language model for video localization and question answering, 2023.
  53. Socratic models: Composing zero-shot multimodal reasoning with language, 2022.
  54. Leveraging video descriptions to learn video question answering, 2016.
  55. Sigmoid loss for language image pre-training, 2023.
  56. A simple llm framework for long-range video question-answering, 2023a.
  57. Mm-narrator: Narrating long-form videos with multimodal in-context learning, 2023b.
  58. Video-llama: An instruction-tuned audio-visual language model for video understanding, 2023c.
  59. Learning video representations from large language models. In arXiv preprint arXiv:2212.04501, 2022.
  60. Efficiently programming large language models using sglang, 2023.
  61. Uncovering temporal context for video question and answering, 2015.
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Authors (5)
  1. Chuyi Shang (1 paper)
  2. Amos You (1 paper)
  3. Sanjay Subramanian (18 papers)
  4. Trevor Darrell (324 papers)
  5. Roei Herzig (34 papers)
Citations (4)
View on Semantic Scholar

Summary

  • The paper presents an iterative multi-LMM agent framework that efficiently navigates video content for enhanced VideoQA performance.
  • It employs a modular approach with traversal, location, evaluation, and replanning phases to strategically extract key video frames.
  • Experimental results on benchmarks like NExT-QA and STAR demonstrate TraveLER’s adaptability and computational efficiency.

TraveLER: Navigating the Challenges of Video Question-Answering with a Multi-LMM Agent Framework

Introduction

The domain of video question-answering (VideoQA) poses unique challenges due to its requirement for temporal and multimodal understanding. Although recent advancements in Large Multimodal Models (LMMs) have showcased promising results, applying these models efficiently and effectively within the VideoQA context remains a complex endeavor. The prevailing methodologies, while advancing the field, often grapple with limitations such as computational inefficiency and the lack of adaptability in discerning and extracting question-relevant information from videos. Addressing these challenges, this blog introduces TraveLER, a novel framework designed to iteratively and intelligently navigate videos, leveraging the strengths of LMMs in an optimized manner for VideoQA tasks.

TraveLER Framework

TraveLER, standing for Traverse, Locate, Evaluate, and Replan, represents a paradigm shift in handling VideoQA by adopting a modular and iterative approach with multiple LMM agents. Each agent within the framework plays a pivotal role in ensuring a comprehensive and efficient extraction of relevant information from key video frames, thus facilitating accurate question answering. Below is a detailed examination of the components that constitute the TraveLER framework:

  • Traversal: The initial phase involves planning a trajectory through the video, determined by a Planner LMM, which devises a strategy based on the question and available video information. This plan outlines the keyframes likely to contain pertinent information for answering the query.
  • Locator: Following the plan, the framework employs both a Retriever and an Extractor to pinpoint and garner information from the specified frames. This process not only involves selecting the frames but also interrogating them with finer-grained questions to draw out detailed insights relevant to the overarching question.
  • Evaluator: Post information extraction, the Evaluator ascertains whether the gathered data suffices to answer the question. If the information proves inadequate or the initial plan remains unfulfilled, the framework enters a replanning phase.
  • Replanning: Leveraging the insights acquired in the previous iteration, the framework refines or extends its initial plan, revisiting the video to collect additional information or to reassess previously identified keyframes.

The iterative nature of TraveLER, with its capacity to dynamically adjust its strategy based on the information acquired at each stage, stands out as a significant advancement over static, one-pass methodologies prevalent in the domain.

Experimental Insights and Implications

The TraveLER framework was rigorously tested against several VideoQA benchmarks, including NExT-QA, STAR, and the Perception Test. The results affirm TraveLER's superiority, largely attributable to its iterative process and modular design, allowing for the strategic selection of frames and the extraction of nuanced information directly relevant to the question. Notably, TraveLER demonstrates improved performance across benchmarks without necessitating task-specific fine-tuning or extensive video annotations, underscoring its adaptability and efficiency.

The Road Ahead

TraveLER’s introduction into the VideoQA domain heralds potential shifts in approach and methodology for future research. Its ability to iteratively refine its search and extraction process, coupled with the exploitation of LMMs without demanding extensive computational resources, lays the groundwork for further explorations into efficient, adaptable models for VideoQA and related tasks.

Conclusion

The imperative for models that can navigate the complexity of video content with precision and adaptability has never been more pronounced. TraveLER, with its multi-LMM agent framework, represents a pivotal step towards addressing the nuanced demands of the VideoQA domain. Through its modular, iterative approach, TraveLER not only enhances the efficiency of information extraction from videos but also opens avenues for research into more adaptable and intelligent multimodal question-answering systems.