Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
60 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
8 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning (2402.11435v1)

Published 18 Feb 2024 in cs.CV

Abstract: LLMs demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension or localization of specific video segments. In light of these challenges, we propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. To support the training of Momentor, we design an automatic data generation engine to construct Moment-10M, a large-scale video instruction dataset with segment-level instruction data. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization. Zero-shot evaluations on several tasks demonstrate that Momentor excels in fine-grained temporally grounded comprehension and localization.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat., 2(4):433–459, 2010.
  2. Tsp: Temporally-sensitive pretraining of video encoders for localization tasks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  3173–3183, 2021.
  3. Unified fully and timestamp supervised temporal action segmentation via sequence to sequence translation. In European Conference on Computer Vision, pp.  52–68. Springer, 2022.
  4. Castellano, B. Pyscenedetect: Intelligent scene cut detection and video splitting tool. https://pyscenedetect.readthedocs.io/en/latest/, 2018.
  5. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023. URL https://lmsys.org/blog/2023-03-30-vicuna/.
  6. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  7. Fast and unsupervised action boundary detection for action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  3323–3332, 2022.
  8. Tall: Temporal activity localization via language query. In Proceedings of the IEEE international conference on computer vision, pp.  5267–5275, 2017.
  9. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pp.  2961–2969, 2017.
  10. Dense-captioning events in videos. In Proceedings of the IEEE international conference on computer vision, pp.  706–715, 2017.
  11. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  780–787, 2014.
  12. Detecting moments and highlights in videos via natural language queries. Advances in Neural Information Processing Systems, 34:11846–11858, 2021.
  13. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023a.
  14. Videochat: Chat-centric video understanding. arXiv preprint arXiv:2305.06355, 2023b.
  15. Univtg: Towards unified video-language temporal grounding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  2794–2804, 2023.
  16. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499, 2023.
  17. Valley: Video assistant with large language model enhanced ability. arXiv preprint arXiv:2306.07207, 2023.
  18. Video-chatgpt: Towards detailed video understanding via large vision and language models. arXiv preprint arXiv:2306.05424, 2023.
  19. OpenAI. Chatgpt: Optimizing language models for dialogue. https://openai.com/blog/chatgpt, 2022. Accessed on: November 30, 2022.
  20. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp.  8748–8763. PMLR, 2021.
  21. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019. URL https://arxiv.org/abs/1908.10084.
  22. Temporally-weighted hierarchical clustering for unsupervised action segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  11225–11234, 2021.
  23. A multi-stream bi-directional recurrent neural network for fine-grained action detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  1961–1970, 2016.
  24. Combining embedded accelerometers with computer vision for recognizing food preparation activities. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp.  729–738, 2013.
  25. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  26. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.
  27. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pp.  1645–1653, 2017.
  28. Vid2seq: Large-scale pretraining of a visual language model for dense video captioning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  10714–10726, 2023.
  29. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp.  9127–9134, 2019.
  30. Merlot reserve: Neural script knowledge through vision and language and sound. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  16375–16387, 2022.
  31. Span-based localizing network for natural language video localization. arXiv preprint arXiv:2004.13931, 2020a.
  32. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv preprint arXiv:2306.02858, 2023.
  33. Learning 2d temporal adjacent networks for moment localization with natural language. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pp.  12870–12877, 2020b.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Long Qian (26 papers)
  2. Juncheng Li (121 papers)
  3. Yu Wu (196 papers)
  4. Yaobo Ye (2 papers)
  5. Hao Fei (105 papers)
  6. Tat-Seng Chua (359 papers)
  7. Yueting Zhuang (164 papers)
  8. Siliang Tang (116 papers)
Citations (22)
View on Semantic Scholar