A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources

arXiv:2608.13183 · cs.CV · Submitted 2026-08-13 · Read on arXiv

cs.CV

Submitted: 2026-08-13

Updated: 2026-09-25

Code: https://github.com/tue-mps/vision-ssl-study

Terminology

• Foundation Model — 9× in this paper · explained in 3 episodes
• Foundation Models — 9× in this paper · explained in 7 episodes
• Representation Learning — 9× in this paper · explained in 2 episodes
• Self-Supervised Learning — 9× in this paper · explained in 7 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 4× in this paper
• Downstream Tasks — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• International Conference on Learning Representations — 3× in this paper
• International Conference on Machine Learning — 3× in this paper
• Resource Constraints — 3× in this paper · explained in 3 episodes
• Self-Supervised Learning (SSL) — 3× in this paper · explained in 4 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 2× in this paper
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• CrossVideoMAE: Self-Supervised Image-Video Representation Learning with Masked Autoencoders
• Scaling 4D Representations
• A Short Note on the Kinetics-700 Human Action Dataset
• Back to Basics: Let Denoising Generative Models Denoise

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models