Papers
1–4 of 4VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization
Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While video provides rich tempora...
Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
Frozen self-supervised representations often transfer well with only a few labels across many semantic tasks. We argue that a single geometric quantity, \emph{directional} CDNV (decision-axis variance...
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g., MAE) that reconstruct raw low-level data, and predictive approaches (e.g., I-JEPA) that predict h...
IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning
Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features. Existing JEAs re...