Papers › Temporal Relations of Informative Frames in Action Recognition
Temporal Relations of Informative Frames in Action Recognition
Alireza Rahnama, Azadeh Mansouri
This paper presents a simple approach leveraging temporal learning on informative frames for action recognition. We propose a training-free simple adaptive frame selection scenario employing just the similarity technique in a temporal window. The proposed frame selection method provides an appropriate strategy to capture informative frames and provide meaningful features. Moreover, we use transfer learning for spatial feature extraction and employ LSTM and GRU for temporal modeling. Our method is evaluated on two popular datasets, UCF11 and KTH, and it demonstrates acceptable results.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Recognition | KTH | CNN-GRU | 16:9 Accuracy | 95.38 | #1 of 1 | Archive leaderboard | report |
| Action Recognition | UCFSports | CNN-LSTM | leave one out cross validation(LOOCV) | 98.27 | #1 of 1 | 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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