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Found 13 papers, 6 papers with code
Intuitive physics understanding emerges from self-supervised pretraining on natural videos
We investigate the emergence of intuitive physics understanding in general-purpose deep neural network models trained to predict masked regions in natural videos.
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
To facilitate a systematic evaluation of VLM progress, we introduce UniBench: a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of carefully categorized capabilities from object recognition to spatial awareness, counting, and much more.
An Introduction to Vision-Language Modeling
Then, we present and discuss approaches to evaluate VLMs.
Learning and Leveraging World Models in Visual Representation Learning
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model.
Revisiting Feature Prediction for Learning Visual Representations from Video
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision.
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering.
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.
Self-supervised learning of Split Invariant Equivariant representations
We hope that both our introduced dataset and approach will enable learning richer representations without supervision in more complex scenarios.
The Robustness Limits of SoTA Vision Models to Natural Variation
We study not only how robust recent state-of-the-art models are, but also the extent to which models can generalize variation in factors when they're present during training.
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid development, with the emergence of many method variations but only few principled guidelines that would help practitioners to successfully deploy them.
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning
This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream.
On the duality between contrastive and non-contrastive self-supervised learning
Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches.
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution.
