title: Mavors: Multi-granularity Video Representation for Multimodal Large
    Language Model
summary: Long-context video understanding in multimodal large language models (MLLMs)
  faces a critical challenge: balancing computational efficiency with the
  retention of fine-grained spatio-temporal patterns. Existing approaches (e.g.,
  sparse sampling, dense sampling with low resolution, and token compression)
  suffer from significant information loss in temporal dynamics, spatial details,
  or subtle interactions, particularly in videos with complex motion or varying
  resolutions. To address this, we propose Mavors, a novel framework
  that introduces Multi-granularity
  video representation for holistic
  long-video modeling. Specifically, Mavors directly encodes raw video content
  into latent representations through two core components: 1) an Intra-chunk
  Vision Encoder (IVE) that preserves high-resolution spatial features via 3D
  convolutions and Vision Transformers, and 2) an Inter-chunk Feature Aggregator
  (IFA) that establishes temporal coherence across chunks using transformer-based
  dependency modeling with chunk-level rotary position encodings. Moreover, the
  framework unifies image and video understanding by treating images as
  single-frame videos via sub-image decomposition. Experiments across diverse
  benchmarks demonstrate Mavors' superiority in maintaining both spatial fidelity
  and temporal continuity, significantly outperforming existing methods in tasks
  requiring fine-grained spatio-temporal reasoning.
paper_authors._id: 67fdd1d7634e600357b5b7ff
paper_authors.name: Yang Shi
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paper_authors.name: Zhenhua Wu
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paper_authors.name: Yuanxing Zhang
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paper_authors.name: Zihao Wang
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paper_authors.name: Weihong Lin
paper_authors._id: 67fdd1d7634e600357b5b806
paper_authors.name: Jingyun Hua
paper_authors._id: 67fdd1d7634e600357b5b807
paper_authors.name: Zekun Wang
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paper_authors.fullname: Zekun Wang
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paper_authors.user: kugwzk
paper_authors._id: 67fdd1d7634e600357b5b808
paper_authors.name: Xinlong Chen
paper_authors._id: 67fdd1d7634e600357b5b809
paper_authors.name: Bohan Zeng
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paper_authors.name: Wentao Zhang
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paper_authors.name: Fuzheng Zhang
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paper_authors.name: Wenjing Yang
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paper_authors.name: Di Zhang
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paper_title: Mavors: Multi-granularity Video Representation for Multimodal Large
    Language Model
paper_summary: Long-context video understanding in multimodal large language models (MLLMs)
  faces a critical challenge: balancing computational efficiency with the
  retention of fine-grained spatio-temporal patterns. Existing approaches (e.g.,
  sparse sampling, dense sampling with low resolution, and token compression)
  suffer from significant information loss in temporal dynamics, spatial details,
  or subtle interactions, particularly in videos with complex motion or varying
  resolutions. To address this, we propose Mavors, a novel framework
  that introduces Multi-granularity
  video representation for holistic
  long-video modeling. Specifically, Mavors directly encodes raw video content
  into latent representations through two core components: 1) an Intra-chunk
  Vision Encoder (IVE) that preserves high-resolution spatial features via 3D
  convolutions and Vision Transformers, and 2) an Inter-chunk Feature Aggregator
  (IFA) that establishes temporal coherence across chunks using transformer-based
  dependency modeling with chunk-level rotary position encodings. Moreover, the
  framework unifies image and video understanding by treating images as
  single-frame videos via sub-image decomposition. Experiments across diverse
  benchmarks demonstrate Mavors' superiority in maintaining both spatial fidelity
  and temporal continuity, significantly outperforming existing methods in tasks
  requiring fine-grained spatio-temporal reasoning.
paper_ai_summary: Mavors, a novel framework for long-context video understanding, uses a multi-granularity video representation to preserve spatial fidelity and temporal continuity through 3D convolutions, Vision Transformers, and transformer-based dependency modeling.
paper_ai_keywords: Intra-chunk Vision Encoder Inter-chunk Feature Aggregator 3D convolutions Vision Transformers transformer-based dependency modeling chunk-level rotary position encodings