arXiv Machine Learning

JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning

JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.

arXiv AI

Sep 25

ReCalMatch:Reliability-Calibrated Semantic Guidance for Semi-Supervised Fine-Grained Recognition

ReCalMatch introduces a reliability‑calibrated semantic framework for semi‑supervised fine‑grained visual recognition, addressing the problem of overconfident pseudo‑label errors that arise when visually similar categories produce incorrect high‑confidence predictions. The method constructs class‑conditioned semantic prototypes from class names and domain‑specific aspects, and computes a visual‑semantic agreement score to calibrate pseudo‑label reliability alongside prediction confidence and entropy. Experiments on datasets such as CUB‑200‑2011, Stanford Dogs, NABirds, and iNaturalist18 demonstrate that ReCalMatch consistently improves strong SSL baselines, especially in low‑label regimes where pseudo‑label noise is most severe.

By Yundi Hong, Hongyang He, Zheng Fang, Xuanyu Liu, Victor Sanchez

rag

arXiv Machine Learning

Jun 16

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605. 09697v3 Announce Type: replace-cross Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples.

By Radhika Amar Desai, Modigari Narendra

rag computer-vision data-engineering

arXiv Machine Learning

Aug 24

Geometric Regularization for Long-Tailed Semi-Supervised Learning via Gaussian Feature Bridges

arXiv:2608.20710v1 Announce Type: new Abstract: Real-world semi-supervised learning (SSL) often encounters significant challenges with long-tailed label distributions and noisy pseudo-labels, which h...

By Hongyang He, Xinyuan Song, Yan Zhong, Daizong Liu, Yanbin Li, Yang-fan He, Wenqiao Zhang

diffusion benchmarks safety

arXiv Computer Vision

Oct 7

Unsupervised Long-Tailed Adaptation of Vision-Language Models

arXiv:2610.07903v1 Announce Type: new Abstract: Adapting vision-language models to downstream tasks has achieved remarkable success by leveraging pseudo-labels generated from unlabeled data. Existing...

By Keliang Chen, Yaxin Hou, Hui Liu, Yuheng Jia

llms multimodal benchmarks safety

arXiv AI

Sep 1

Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning

arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...

By Yue Cheng, Jiajun Zhang, Xiaohui Gao, Weiwei Xing, Zhanxing Zhu

efficiency benchmarks safety

arXiv AI

Jun 11

CoVar: Confidence-Variance-Guided Pseudo-Label Selection for Semi-Supervised Learning

arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.

By Jinshi Liu, Lei He, Pan Liu

computer-vision benchmarks

arXiv Machine Learning

Jul 27

Unbiased Open World Regularization for Fair Self-Supervised Learning

arXiv:2607. 22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset.

By L{\'e}o Nicollier (CB, ATT), Marc Pic (ATT), Pablo Mus{\'e} (CB, IFUMI), Enric Meinhardt-Llopis (CB), Gabriele Facciolo (CB)

rag diffusion benchmarks safety

arXiv Computer Vision

Sep 14

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

arXiv:2609.12552v1 Announce Type: new Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left t...

By Ma\"elic Neau

rag computer-vision multimodal benchmarks

arXiv AI

Aug 20

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.

By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong

llms rag computer-vision multimodal benchmarks safety

arXiv Computer Vision

Sep 22

Training-Free Spectral Transductive Refinement for Cross-Domain Few-Shot Classification

The paper introduces Spectral Transductive Refinement (STR), a training‑free method that refines class prototypes at test time using the geometry of a joint k‑nearest‑neighbour graph and a normalized‑Laplacian spectral coordinate system. STR operates solely on frozen visual embeddings, iteratively updating pseudo‑labelled queries to improve one‑shot and few‑shot classification under domain shift. Experiments on ResNet‑18 and ResNet‑10 backbones show STR outperforms single‑prototype baselines and rivals meta‑trained cross‑domain few‑shot methods, achieving the best 1‑shot average across eight target domains.

By Fahim Rahman, S. M. Tanjeeb Meheran Rohan, Md. Taimum Ibne Sayed, Asaduzzaman Herok, Md. Bakhtiar Hasan

rag benchmarks

arXiv Computer Vision

Oct 7

Unlocking Fine-Grained Perception in CLIP via Structurally-Aware Latent Masked Modeling

arXiv:2610.07689v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense p...

By Juntong Li, Lingwei Dang, Haomin Wu, Ziyan Qiu, Qingxin Xiao, Qingyao Wu

llms rag efficiency multimodal safety

arXiv Machine Learning

Jul 21

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations

arXiv:2607. 16725v1 Announce Type: cross Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant.

By Changyu Liu, Yuling Jiao, Jian Huang

safety

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