Image Classifiers are Efficient Self-Supervised Video Representation Learners

arXiv:2609.40347 · cs.CV, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Project page: https://cvir.github.io/projects/videomsn

Terminology

• Accuracy — 33× in this paper · explained in 1 episode
• Representation Learning — 27× in this paper · explained in 2 episodes
• Transformer — 24× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 9× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 8× in this paper
• Masked Siamese Network — 8× in this paper
• Regularization — 8× in this paper · explained in 3 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Distillation — 7× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Masked Image Modeling — 6× in this paper
• Self-Supervised Learning — 5× in this paper · explained in 8 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Information Leakage — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction
• A Convex Relaxation for Weakly Supervised Classifiers
• The Kinetics Human Action Video Dataset
• Decoupled Weight Decay Regularization
• CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
• R-MAE: Regions Meet Masked Autoencoders
• DINOv3
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• AVD: Adversarial Video Distillation

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