VideoMSN: Turning Image Classifiers into Efficient Video Learners via Super-Images
免 3D 結構與重構解碼器！VideoMSN 利用超大型圖片將影像分類器轉化為高效視訊學習器
This paper introduces VideoMSN, a decoder-free Masked Siamese Network framework that reframes video representation learning. By formatting video frames into grid-like 'super images', VideoMSN leverages standard image ViTs (like DINO-v3 or DeiT-v3) with two distinct masking strategies: spatial patch masking and temporal frame masking. This design prevents cross-frame info leakage and aligns embeddings using a masked Siamese loss. VideoMSN achieves state-of-the-art performance on UCF101 and Kinetics-400 while requiring up to 160x fewer video pretraining epochs than existing baselines.
Key points
Super-Image Grid Formatting
Converts video frames into a single grid-based 'super image', allowing standard image ViTs to process videos without modifying their architecture.
Dual Masking Strategy
Creates two views (spatial patch masking and temporal frame masking) to prevent information leakage and force the model to capture both motion and appearance cues.
Decoder-Free Alignment
Eliminates expensive reconstruction decoders by directly aligning embeddings of the masked views using a shared ViT encoder and a Siamese loss.
Up to 160x Training Efficiency
VideoMSN achieves state-of-the-art results on Kinetics-400 while requiring up to 32x and 160x fewer video pretraining epochs than prior methods.
How it works
Why it matters
Video self-supervised learning typically demands massive compute due to heavy 3D architectures or generative decoders. VideoMSN proves that with clever data representation (super images) and masking, powerful pretrained image models can be directly and efficiently repurposed for video. This significantly lowers the barrier to entry for video AI development and yields highly transferable features beneficial for low-shot video classification scenarios.
Who it affects
- AI Developer
- AI Researcher
- Enterprise Leader
How to use it
- 1Low-shot and resource-constrained video classification
- 2Rapid adaptation of pretrained image foundation models to video feature extraction
Limitations & caveats
- Representing videos as grid-like super-images may limit very long video or high-frame-rate modeling due to grid resolution constraints.
- Performance depends heavily on the quality and representation power of the starting pretrained image foundation models.
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