Papers › OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video...
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
Tongjia Chen, Hongshan Yu, Zhengeng Yang, Zechuan Li, Wei Sun, Chen Chen
Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names, leading to less distinct semantic space and potential performance limitations. In this work, we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names, we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover, to assign the best descriptors with different video instances, we propose Optimal Descriptor Solver, forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot, few-shot, and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.
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Code
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Code Syntology ran Syntology
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Tasks
2 archive task tags without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Action Recognition | HMDB51 | OST | Top-1 Accuracy | 55.9 | #9 of 29 | Archive leaderboard | report |
| Zero-Shot Action Recognition | Kinetics | OST | Top-1 Accuracy | 75.1 | #3 of 20 | Archive leaderboard | report |
| Zero-Shot Action Recognition | Kinetics | OST | Top-5 Accuracy | 94.6 | #3 of 20 | Archive leaderboard | report |
| Zero-Shot Action Recognition | UCF101 | OST | Top-1 Accuracy | 79.7 | #10 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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