Papers › Motion Feature Network: Fixed Motion Filter for Action Recognition
Motion Feature Network: Fixed Motion Filter for Action Recognition
Myunggi Lee, Seungeui Lee, Sungjoon Son, Gyu-tae Park, Nojun Kwak
Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.
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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 | Something-Something V1 | Motion Feature Net | Top 1 Accuracy | 43.9 | #70 of 74 | Archive leaderboard | report |
| Action Recognition In Videos | Jester (Gesture Recognition) | MFNet | Val | 96.68 | #3 of 9 | Archive leaderboard | report |
| Action Recognition In Videos | Something-Something V1 | Motion Feature Net | Top 1 Accuracy | 43.9 | #2 of 3 | 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.
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