Papers › MVFNet: Multi-View Fusion Network for Efficient Video Recognition
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
Wenhao Wu, Dongliang He, Tianwei Lin, Fu Li, Chuang Gan, Errui Ding
Conventionally, spatiotemporal modeling network and its complexity are the two most concentrated research topics in video action recognition. Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance. In this paper, we attempt to acquire both efficiency and effectiveness simultaneously. First of all, besides traditionally treating H x W x T video frames as space-time signal (viewing from the Height-Width spatial plane), we propose to also model video from the other two Height-Time and Width-Time planes, to capture the dynamics of video thoroughly. Secondly, our model is designed based on 2D CNN backbones and model complexity is well kept in mind by design. Specifically, we introduce a novel multi-view fusion (MVF) module to exploit video dynamics using separable convolution for efficiency. It is a plug-and-play module and can be inserted into off-the-shelf 2D CNNs to form a simple yet effective model called MVFNet. Moreover, MVFNet can be thought of as a generalized video modeling framework and it can specialize to be existing methods such as C2D, SlowOnly, and TSM under different settings. Extensive experiments are conducted on popular benchmarks (i.e., Something-Something V1 & V2, Kinetics, UCF-101, and HMDB-51) to show its superiority. The proposed MVFNet can achieve state-of-the-art performance with 2D CNN's complexity.
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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 | MVFNet-ResNet101 (ensemble, ImageNet pretrained, RGB only) | Acc@1 | 79.1 | #119 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | MVFNet-ResNet101 (ensemble, ImageNet pretrained, RGB only) | Acc@5 | 93.8 | #119 of 207 | Archive leaderboard | report |
| Action Recognition | Something-Something V1 | MVFNet-R50EN | Top 1 Accuracy | 54.0 | #35 of 74 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | MVFNet-ResNet50 (center crop, 8+16 ensemble, ImageNet pretrained, RGB only) | Top-1 Accuracy | 66.3 | #81 of 123 | 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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