Papers › MARS: Motion-Augmented RGB Stream for Action Recognition
MARS: Motion-Augmented RGB Stream for Action Recognition
Nieves Crasto, Philippe Weinzaepfel, Karteek Alahari, Cordelia Schmid
Most state-of-the-art methods for action recognition consist of a two-stream architecture with 3D convolutions: an appearance stream for RGB frames and a motion stream for optical flow frames. Although combining flow with RGB improves the performance, the cost of computing accurate optical flow is high, and increases action recognition latency. This limits the usage of two-stream approaches in real-world applications requiring low latency. In this paper, we introduce two learning approaches to train a standard 3D CNN, operating on RGB frames, that mimics the motion stream, and as a result avoids flow computation at test time. First, by minimizing a feature-based loss compared to the Flow stream, we show that the network reproduces the motion stream with high fidelity. Second, to leverage both appearance and motion information effectively, we train with a linear combination of the feature-based loss and the standard cross-entropy loss for action recognition. We denote the stream trained using this combined loss as Motion-Augmented RGB Stream (MARS). As a single stream, MARS performs better than RGB or Flow alone, for instance with 72.7% accuracy on Kinetics compared to 72.0% and 65.6% with RGB and Flow streams respectively.
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 Classification | Kinetics-400 | MARS+RGB+Flow (64 frames) | Acc@1 | 74.9 | #159 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | MARS+RGB+Flow (16 frames) | Acc@1 | 68.9 | #182 of 207 | Archive leaderboard | report |
| Action Classification | MiniKinetics | MARS+RGB+Flow (16 frames) | Top-1 Accuracy | 73.5 | #1 of 1 | Archive leaderboard | report |
| Action Recognition | HMDB-51 | MARS+RGB+FLow (64 frames, Kinetics pretrained) | Average accuracy of 3 splits | 80.9 | #19 of 77 | Archive leaderboard | report |
| Action Recognition | Something-Something V1 | MARS+RGB+Flow (64 frames, Kinetics pretrained) | Top 1 Accuracy | 53.0 | #39 of 74 | Archive leaderboard | report |
| Action Recognition | Something-Something V1 | MARS+RGB+Flow (16 frames, Kinetics pretrained) | Top 1 Accuracy | 40.4 | #73 of 74 | Archive leaderboard | report |
| Action Recognition | UCF101 | MARS+RGB+Flow (64 frames, Kinetics pretrained) | 3-fold Accuracy | 97.8 | #13 of 91 | Archive leaderboard | report |
| Action Recognition | UCF101 | MARS+RGB+Flow (16 frames) | 3-fold Accuracy | 95.8 | #41 of 91 | 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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