Papers › A CLIP-Hitchhiker's Guide to Long Video Retrieval
A CLIP-Hitchhiker's Guide to Long Video Retrieval
Max Bain, Arsha Nagrani, Gül Varol, Andrew Zisserman
Our goal in this paper is the adaptation of image-text models for long video retrieval. Recent works have demonstrated state-of-the-art performance in video retrieval by adopting CLIP, effectively hitchhiking on the image-text representation for video tasks. However, there has been limited success in learning temporal aggregation that outperform mean-pooling the image-level representations extracted per frame by CLIP. We find that the simple yet effective baseline of weighted-mean of frame embeddings via query-scoring is a significant improvement above all prior temporal modelling attempts and mean-pooling. In doing so, we provide an improved baseline for others to compare to and demonstrate state-of-the-art performance of this simple baseline on a suite of long video retrieval benchmarks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Action Recognition | Charades | CLIP-Hitchhiker (ViT-B/16, 32 frames) | mAP | 21.1 | #4 of 4 | Archive leaderboard | report |
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Methods
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