Papers › Leveraging Temporal Contextualization for Video Action Recognition
Leveraging Temporal Contextualization for Video Action Recognition
Minji Kim, Dongyoon Han, Taekyung Kim, Bohyung Han
We propose a novel framework for video understanding, called Temporally Contextualized CLIP (TC-CLIP), which leverages essential temporal information through global interactions in a spatio-temporal domain within a video. To be specific, we introduce Temporal Contextualization (TC), a layer-wise temporal information infusion mechanism for videos, which 1) extracts core information from each frame, 2) connects relevant information across frames for the summarization into context tokens, and 3) leverages the context tokens for feature encoding. Furthermore, the Video-conditional Prompting (VP) module processes context tokens to generate informative prompts in the text modality. Extensive experiments in zero-shot, few-shot, base-to-novel, and fully-supervised action recognition validate the effectiveness of our model. Ablation studies for TC and VP support our design choices. Our project page with the source code is available at https://github.com/naver-ai/tc-clip
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Code
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Code Syntology ran Syntology
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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 | HMDB51 | TC-CLIP | Top-1 Accuracy | 56.0 | #8 of 29 | Archive leaderboard | report |
| Zero-Shot Action Recognition | Kinetics | TC-CLIP | Top-1 Accuracy | 78.1 | #1 of 20 | Archive leaderboard | report |
| Zero-Shot Action Recognition | Kinetics | TC-CLIP | Top-5 Accuracy | 95.7 | #1 of 20 | Archive leaderboard | report |
| Zero-Shot Action Recognition | UCF101 | TC-CLIP | Top-1 Accuracy | 85.4 | #7 of 35 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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