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LLaVA-MR: Large Language-and-Vision Assistant for Video Moment Retrieval

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arxiv 2411.14505 v1 pith:3PT6EIIR submitted 2024-11-21 cs.CV

Weiheng Lu , Jian Li , An Yu , Ming-Ching Chang , Shengpeng Ji , Min Xia This is my paper · ORCID
classification cs.CV
keywords llava-mrmomentretrievalframelargeassistantcontextextraction
verification ladder T0 review T1 audit T2 compute T3 formal
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Multimodal Large Language Models (MLLMs) are widely used for visual perception, understanding, and reasoning. However, long video processing and precise moment retrieval remain challenging due to LLMs' limited context size and coarse frame extraction. We propose the Large Language-and-Vision Assistant for Moment Retrieval (LLaVA-MR), which enables accurate moment retrieval and contextual grounding in videos using MLLMs. LLaVA-MR combines Dense Frame and Time Encoding (DFTE) for spatial-temporal feature extraction, Informative Frame Selection (IFS) for capturing brief visual and motion patterns, and Dynamic Token Compression (DTC) to manage LLM context limitations. Evaluations on benchmarks like Charades-STA and QVHighlights demonstrate that LLaVA-MR outperforms 11 state-of-the-art methods, achieving an improvement of 1.82% in R1@0.5 and 1.29% in mAP@0.5 on the QVHighlights dataset. Our implementation will be open-sourced upon acceptance.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DisTime: Distribution-based Time Representation for Video Large Language Models

    cs.CV 2025-05 conditional novelty 7.0 of 10

    A single learnable time token, decoded into a probability distribution over time bins, improves temporal grounding in Video-LLMs and is trained partly on a new 1.25M-event pseudo-labeled dataset.

  2. Sparse-Dense Side-Tuner for efficient Video Temporal Grounding

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SDST is a parameter-efficient, anchor-free side-tuning architecture for video temporal grounding that matches or beats state-of-the-art methods with about 73% fewer trainable parameters.

  3. SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM

    cs.CV 2025-11 conditional novelty 5.0 of 10

    SMART, an audio-enhanced MLLM with shot-aware token compression, reports new state-of-the-art moment retrieval accuracy on Charades-STA and QVHighlights.

  4. A Survey on Video Temporal Grounding with Multimodal Large Language Model

    cs.CV 2025-08 unverdicted novelty 3.0 of 10

    A taxonomized review of video temporal grounding with multimodal large language models, covering model roles, training paradigms, feature processing, benchmarks, and open problems.

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