Abstract
We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of relying on heavy 3D architectures or reconstruction-based autoencoders for learning with unlabeled data, we repurpose standard image Vision Transformers by representing videos as super images which are grids composed of frames sampled from videos. From each super image, we construct two views: one with spatial patch masking and the other with temporal frame masking, ensuring no information leakage across frames. A shared Vision Transformer (ViT) encoder aligns their embeddings using a masked Siamese loss, capturing both motion and appearance cues without reconstruction. Our decoder-free formulation leverages an image foundation model towards efficient video representation learning. Starting from pretrained DINO-v3 and DeiT-v3 image encoders, VideoMSN achieves state-of-the-art performance on Kinetics-400, UCF101, and HMDB51 while requiring up to $32\times$ fewer and $160\times$ fewer video pretraining epochs compared to prior video self-supervised learning methods. Our proposed approach also shows strong performance in low-shot classification, confirming the transferability of the learned representations in a label-scarce scenario. Project Page: https://cvir.github.io/projects/videomsn.
Keywords
Publication details
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- Green open access
Cite this article
APA 7
Iqbal, O., Sarkar, S., Marjit, S., Chakraborty, O., Chakraborty, A., & Das, A. (2026). Image Classifiers are Efficient Self-Supervised Video Representation Learners. https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners
MLA 9
Iqbal, Owais, et al. "Image Classifiers are Efficient Self-Supervised Video Representation Learners." https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners.
Chicago (author–date)
Iqbal, Owais, Sudipta Sarkar, Shyam Marjit, Omprakash Chakraborty, Anirban Chakraborty, and Abir Das. 2026. "Image Classifiers are Efficient Self-Supervised Video Representation Learners." https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners.
Harvard
Iqbal, O., Sarkar, S., Marjit, S., Chakraborty, O., Chakraborty, A. and Das, A. (2026) 'Image Classifiers are Efficient Self-Supervised Video Representation Learners', Available at: https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners.
Vancouver
Iqbal O, Sarkar S, Marjit S, Chakraborty O, Chakraborty A, Das A. Image Classifiers are Efficient Self-Supervised Video Representation Learners. https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners
IEEE
O. Iqbal, S. Sarkar, S. Marjit, O. Chakraborty, A. Chakraborty, and A. Das, "Image Classifiers are Efficient Self-Supervised Video Representation Learners," https://omanscience.com/en/articles/image-classifiers-are-efficient-self-supervised-video-representation-learners.