{
  "annotation_id": "69a5a206-1f96-4acc-adb7-e45a970fd994",
  "date_created": "2026-02-17T05:53:04.898000Z",
  "date_modified": "2026-02-17T05:53:04.898000Z",
  "file_hash": "3fe6d587ac0d77b1a29330d936bf09129a8c58b961698ef5721a62f8a413364c",
  "private": false,
  "record": {
    "abstract": "Long videos, ranging from minutes to hours, present significant challenges for current Multi-modal Large Language Models (MLLMs) due to their complex events, diverse scenes, and long-range dependencies. Direct encoding of such videos is computationally too expensive, while simple video-to-text conversion often results in redundant or fragmented content. To address these limitations, we introduce MMViR, a novel multi-modal, multi-grained structured representation for long video understanding. MMViR identifies key turning points to segment the video and constructs a three-level description that couples global narratives with fine-grained visual details. This design supports efficient query-based retrieval and generalizes well across various scenarios. Extensive evaluations across three tasks, including QA, summarization, and retrieval, show that MMViR outperforms the prior strongest method, achieving a 19.67% improvement in hour-long video understanding while reducing processing latency to 45.4% of the original.",
    "arxiv_id": "2601.05495",
    "authors": [
      "Zizhong Li",
      "Haopeng Zhang",
      "Jiawei Zhang"
    ],
    "categories": [
      "cs.CV",
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "MMViR: A Multi-Modal and Multi-Granularity Representation for Long-range Video Understanding",
    "url": "https://arxiv.org/abs/2601.05495",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f80dc889-a0c4-408b-ba77-d85cf4372e53",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}