DrVideo: Document Retrieval Based Long Video Understanding

• URL: http://arxiv.org/abs/2406.12846v2
• Date: Sun, 24 Nov 2024 04:33:25 GMT
• Title: DrVideo: Document Retrieval Based Long Video Understanding
• Authors: Ziyu Ma, Chenhui Gou, Hengcan Shi, Bin Sun, Shutao Li, Hamid Rezatofighi, Jianfei Cai,
• Abstract summary: DrVideo is a document-retrieval-based system designed for long video understanding. It first transforms a long video into a coarse text-based long document to retrieve key frames and then updates the documents with the augmented key frame information. It then employs an agent-based iterative loop to continuously search for missing information and augment the document until sufficient question-related information is gathered.
• Score: 44.34473173458403
• License:
• Abstract: Most of the existing methods for video understanding primarily focus on videos only lasting tens of seconds, with limited exploration of techniques for handling long videos. The increased number of frames in long videos poses two main challenges: difficulty in locating key information and performing long-range reasoning. Thus, we propose DrVideo, a document-retrieval-based system designed for long video understanding. Our key idea is to convert the long-video understanding problem into a long-document understanding task so as to effectively leverage the power of large language models. Specifically, DrVideo first transforms a long video into a coarse text-based long document to initially retrieve key frames and then updates the documents with the augmented key frame information. It then employs an agent-based iterative loop to continuously search for missing information and augment the document until sufficient question-related information is gathered for making the final predictions in a chain-of-thought manner. Extensive experiments on long video benchmarks confirm the effectiveness of our method. DrVideo significantly outperforms existing LLM-based state-of-the-art methods on EgoSchema benchmark (3 minutes), MovieChat-1K benchmark (10 minutes), and the long split of Video-MME benchmark (average of 44 minutes).

Related papers

• SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis [52.050036778325094]
  We introduce SALOVA: Segment-Augmented Video Assistant, a novel video-LLM framework designed to enhance the comprehension of lengthy video content. We present a high-quality collection of 87.8K long videos, each densely captioned at the segment level to enable models to capture scene continuity and maintain rich context. Our framework mitigates the limitations of current video-LMMs by allowing for precise identification and retrieval of relevant video segments in response to queries.
  arXiv  Detail & Related papers  (2024-11-25T08:04:47Z)
• Goldfish: Vision-Language Understanding of Arbitrarily Long Videos [51.547065479762715]
  We present a methodology tailored for comprehending videos of arbitrary lengths. We also introduce the TVQA-long benchmark, designed to evaluate models' capabilities in understanding long videos with questions in both vision and text content. Our results indicate that our models have significant improvements in both long and short-video understanding.
  arXiv  Detail & Related papers  (2024-07-17T15:59:32Z)
• Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs [20.168429351519055]
  Video understanding is a crucial next step for multimodal large language models (LMLMs) We propose VideoNIAH (Video Needle In A Haystack), a benchmark construction framework through synthetic video generation. We conduct a comprehensive evaluation of both proprietary and open-source models, uncovering significant differences in their video understanding capabilities.
  arXiv  Detail & Related papers  (2024-06-13T17:50:05Z)
• LVBench: An Extreme Long Video Understanding Benchmark [38.839913137854104]
  We introduce LVBench, a benchmark specifically designed for long video understanding. Our dataset comprises publicly sourced videos and encompasses a diverse set of tasks aimed at long video comprehension and information extraction.
  arXiv  Detail & Related papers  (2024-06-12T09:36:52Z)
• VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos [67.78336281317347]
  Long-form video understanding has been a challenging task due to the high redundancy in video data. We propose VideoTree, a training-free framework which builds a query-adaptive and hierarchical video representation. Our experiments show that our framework improves both reasoning accuracy and efficiency compared to existing methods.
  arXiv  Detail & Related papers  (2024-05-29T15:49:09Z)
• Koala: Key frame-conditioned long video-LLM [70.52369588364992]
  We propose a lightweight and self-supervised long video-LLM (Koala) to adapt pretrained vLLMs for generalizing to longer videos. Our approach outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly, we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.
  arXiv  Detail & Related papers  (2024-04-05T18:33:04Z)
• LongVLM: Efficient Long Video Understanding via Large Language Models [55.813206751150716]
  LongVLM is a simple yet powerful VideoLLM for long video understanding. We encode video representations that incorporate both local and global information. Our model produces more precise responses for long video understanding.
  arXiv  Detail & Related papers  (2024-04-04T11:33:29Z)
• LVCHAT: Facilitating Long Video Comprehension [25.395689904747965]
  We propose Long Video Chat (LVChat) to enable multimodal large language models (LLMs) to read videos. LV significantly outperforms existing methods by up to 27% in accuracy on long-video QA datasets and long-video captioning benchmarks.
  arXiv  Detail & Related papers  (2024-02-19T11:59:14Z)
• Query-aware Long Video Localization and Relation Discrimination for Deep Video Understanding [15.697251303126874]
  Deep Video Understanding (DVU) Challenge aims to push the boundaries of multimodal extraction, fusion, and analytics. This paper introduces a query-aware method for long video localization and relation discrimination, leveraging an imagelanguage pretrained model. Our approach achieved first and fourth positions for two groups of movie-level queries.
  arXiv  Detail & Related papers  (2023-10-19T13:26:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.