arXiv AI
Jul 15
The TIME Machine: On The Power of Motion for Efficient Perception
arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.
You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences
arXiv:2606. 15956v1 Announce Type: cross Abstract: Progress in AI has largely been driven by methods that assume less.
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arXiv Machine Learning
Aug 4
Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data
arXiv:2608. 01336v1 Announce Type: cross Abstract: Modern autonomous-driving fleets record far more video than human reviewers can inspect.
arXiv AI
Jun 9
What Makes Video World Model Latents Action-Relevant: Prediction over Reconstruction
arXiv:2606. 07687v1 Announce Type: cross Abstract: Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-relevant structure in their latent spaces.
arXiv Machine Learning
Aug 7
Observation-Grounded Self-Predictive Reinforcement Learning for Visual Continuous Control
arXiv:2608. 05989v1 Announce Type: new Abstract: Sample-efficient policy learning from pixels is a long-standing challenge in reinforcement learning (RL).
arXiv AI
Aug 5
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
arXiv:2608. 03244v1 Announce Type: new Abstract: Image-goal visual navigation is a fundamental capability for embodied agents.
arXiv Machine Learning
Jul 7
Is Generation Required for Data-Efficient Perception?
arXiv:2512. 08854v3 Announce Type: replace-cross Abstract: It has been hypothesized that achieving the data efficiency of human visual perception requires a generative approach in which internal representations result from inverting a decoder.
arXiv Machine Learning
Jul 3
Object-centric LeJEPA
arXiv:2607. 02404v1 Announce Type: cross Abstract: Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets.
arXiv AI
Jul 7
Mask-based Predictive Representations for Reinforcement Learning
arXiv:2607. 04153v1 Announce Type: cross Abstract: Vision-based deep reinforcement learning involves dealing with high-dimensional inputs of image information.
arXiv Machine Learning
Jul 2
Task-Relevant Representation Decoupling for Visual Reinforcement Learning Generalization
arXiv:2607. 00796v1 Announce Type: new Abstract: Visual Reinforcement Learning (VRL) has achieved considerable success in solving control tasks.
Hugging Face Trending Papers
Aug 4
UniNav: A Unified World-Action Diffusion Model for Visual Navigation
Image-goal visual navigation is a fundamental capability for embodied agents. Existing navigation policies efficiently predict waypoint trajectories but lack visual foresight, while navigation world models can anticipate future observations but often require costly planning rollouts.
arXiv AI
Aug 11
Three Necessary Principles for Self-Supervised Visual Representation Learning
arXiv:2608. 08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy.
Hugging Face Trending Papers
Aug 6
Observation-Grounded Self-Predictive Reinforcement Learning for Visual Continuous Control
Sample-efficient policy learning from pixels is a long-standing challenge in reinforcement learning (RL). Recent dynamics-based representation learning methods have significantly improved the sample efficiency of model-free visual RL by learning dynamics-aware representations through auxiliary prediction performed either in latent space (self-prediction) or observation space (observation prediction).