arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.
By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys
arXiv:2609.24125v1 Announce Type: new
Abstract: Contrastive self-supervised learning has achieved strong performance by learning representations from multiple augmented views of the same image. Howev...
By Akshit Nanda, Shahzad Ahmad, Ram Prasad Padhy
The paper introduces Anchor Divergences, a technique that leverages contrastive learning, exponential families, and information geometry to define context‑specific semantic geometries on fixed vector representations. By establishing a correspondence between probability distributions over anchors and Bregman geometries, the method allows the geometry itself to be modeled through the anchor distribution. Experiments on retrieval tasks demonstrate that Anchor Divergences effectively and efficiently capture context‑dependent semantic similarity.
By Akash Kannan, Kiho Park, Victor Veitch
arXiv:2605.07786v4 Announce Type: replace-cross
Abstract: As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution me...
By Caterina Gallegati, Monica Bianchini, Franco Scarselli, Vittorio Murino, Barbara Toniella Corradini
The paper investigates whether supervised contrastive learning objectives produce a regular simplex geometry in representation space when trained on long‑tailed data. It shows that existing objectives fail to achieve this geometry because they depend on class sample counts. The authors propose a fully balanced contrastive objective that provably forms a regular simplex, leading to a more balanced representation space and improved performance on long‑tailed recognition and transfer learning tasks.
By Joonhyeok Jeong, Jy-yong Sohn, Kibok Lee
The paper introduces a new framework for unsupervised visible‑infrared person re‑identification that leverages modality‑unified prototypes. By contrasting with prototypes that unify both modalities, the method jointly optimizes similarity within and across modalities, improving modality invariance. A self‑distillation step refines instance‑prototype relationships using a steady teacher, resulting in a simple yet effective model validated on standard VI‑ReID benchmarks.
By Menglin Wang, Xiaojin Gong