REEF: Relevance-Aware and Efficient LLM Adapter for Video Understanding

• URL: http://arxiv.org/abs/2504.05491v1
• Date: Mon, 07 Apr 2025 20:36:34 GMT
• Title: REEF: Relevance-Aware and Efficient LLM Adapter for Video Understanding
• Authors: Sakib Reza, Xiyun Song, Heather Yu, Zongfang Lin, Mohsen Moghaddam, Octavia Camps,
• Abstract summary: Recent methods often compress memory banks to handle untemporal videos for video-level understanding.<n>To this, we designed video to compress un videos on a large scale using visual tokens.
• Score: 2.309018557701645
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Integrating vision models into large language models (LLMs) has sparked significant interest in creating vision-language foundation models, especially for video understanding. Recent methods often utilize memory banks to handle untrimmed videos for video-level understanding. However, they typically compress visual memory using similarity-based greedy approaches, which can overlook the contextual importance of individual tokens. To address this, we introduce an efficient LLM adapter designed for video-level understanding of untrimmed videos that prioritizes the contextual relevance of spatio-temporal tokens. Our framework leverages scorer networks to selectively compress the visual memory bank and filter spatial tokens based on relevance, using a differentiable Top-K operator for end-to-end training. Across three key video-level understanding tasks$\unicode{x2013}$ untrimmed video classification, video question answering, and video captioning$\unicode{x2013}$our method achieves competitive or superior results on four large-scale datasets while reducing computational overhead by up to 34%. The code will be available soon on GitHub.

Related papers

• Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding [12.215829700340988]
  Video-XL-Pro is an efficient method for extremely long video understanding. Video-XL-Pro can process over 8K frames on a single A100 GPU.
  arXiv  Detail & Related papers  (2025-03-24T09:21:48Z)
• QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension [86.0749609778104]
  We propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models.<n>QuoTA strategically allocates frame-level importance scores based on query relevance.<n>We decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring.
  arXiv  Detail & Related papers  (2025-03-11T17:59:57Z)
• ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding [55.320254859515714]
  ReTaKe enables VideoLLMs to process 8 times longer frames (up to 2048), similar-sized models by 3-5% and even rivaling much larger ones on VideoMME, MLVU, LongVideoBench, and LVBench.<n>Our code is available at https://github.com/SCZwangxiao/video-ReTaKe.
  arXiv  Detail & Related papers  (2024-12-29T15:42:24Z)
• AdaCM$^2$: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction [10.579335027350263]
  AdaCM$2$ is an adaptive cross-modality memory reduction approach to video-text alignment on video streams.<n>It achieves a 4.5% improvement across multiple tasks in the LVU dataset with a GPU memory consumption reduction of up to 65%.
  arXiv  Detail & Related papers  (2024-11-19T18:04:13Z)
• Visual Context Window Extension: A New Perspective for Long Video Understanding [45.134271969594614]
  We tackle the challenge of long video understanding from the perspective of context windows. We propose to adapt LMMs for long video understanding tasks by extending the visual context window. Our method consistently improves the performance as the number of video frames increases.
  arXiv  Detail & Related papers  (2024-09-30T07:25:16Z)
• VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation [66.00245701441547]
  We introduce a novel approach to reduce vision compute by leveraging redundant vision tokens "skipping layers" rather than decreasing the number of vision tokens. Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video.
  arXiv  Detail & Related papers  (2024-08-29T17:21:58Z)
• Streaming Long Video Understanding with Large Language Models [83.11094441893435]
  VideoStreaming is an advanced vision-language large model (VLLM) for video understanding. It capably understands arbitrary-length video with a constant number of video streaming tokens encoded and propagatedly selected. Our model achieves superior performance and higher efficiency on long video benchmarks.
  arXiv  Detail & Related papers  (2024-05-25T02:22:09Z)
• 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)
• A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames [57.758863967770594]
  We build on the common paradigm of transferring large-scale, image--text models to video via shallow temporal fusion.<n>We expose two limitations to the approach: (1) decreased spatial capabilities, likely due to poor video--language alignment in standard video datasets, and (2) higher memory consumption, bottlenecking the number of frames that can be processed.
  arXiv  Detail & Related papers  (2023-12-12T16:10:19Z)
• Long Video Understanding with Learnable Retrieval in Video-Language Models [36.793956806567834]
  We introduce a learnable retrieval-based video-language model (R-VLM) for efficient long video understanding.<n>Specifically, given a question (Query) and a long video, our model identifies and selects the most relevant K video chunks.<n>This effectively reduces the number of video tokens, eliminates noise interference, and enhances system performance.
  arXiv  Detail & Related papers  (2023-12-08T09:48:36Z)
• VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning [82.09856883441044]
  Video understanding relies on perceiving the global content modeling its internal connections. We propose a block-wise strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture global content.
  arXiv  Detail & Related papers  (2021-06-21T16:48:19Z)

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.