ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound
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Abstract (arXiv)
We introduce an audiovisual method for long-range text-to-video retrieval. Unlike previous approaches designed for short video retrieval (e.g., 5-15 seconds in duration), our approach aims to retrieve minute-long videos that capture complex human actions. One challenge of standard video-only approaches is the large computational cost associated with processing hundreds of densely extracted frames from such long videos. To address this issue, we propose to replace parts of the video with compact audio cues that succinctly summarize dynamic audio events and are cheap to process. Our method, named ECLIPSE (Efficient CLIP with Sound Encoding), adapts the popular CLIP model to an audiovisual video setting, by adding a unified audiovisual transformer block that captures complementary cues from the video and audio streams. In addition to being 2.92x faster and 2.34x memory-efficient than long-range video-only approaches, our method also achieves better text-to-video retrieval accuracy on several diverse long-range video datasets such as ActivityNet, QVHighlights, YouCook2, DiDeMo and Charades.
Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). Builds on 77 of its 89 references that are in the map. First posted on arXiv 6 Apr 2022.
Builds on
77 of 89 references.
References · 77
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50 most cited of 77 All 77 (JSON)
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Citing papers per year: 2022: 8; 2023: 10; 2024: 10; 2025: 7; 2026: 4.
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