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.
By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
arXiv:2610.20412v1 Announce Type: new
Abstract: To reach top accuracy, current fully supervised instance segmentation methods severely rely on large-scale pixel-wise labeled datasets. They are usuall...
By Linwei Chen, Ying Fu, Shaodi You, Hongzhe Liu
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
The paper presents a semi‑supervised framework that uses open‑vocabulary object detectors to generate region proposals and semantic representations, builds an object‑level similarity graph, and propagates probabilistic labels to assign soft, uncertainty‑aware annotations to previously unlabeled objects. Experiments on four detection benchmarks show that high‑quality soft labels can be produced with only a small fraction of manual annotations, and models trained on these semi‑automatically annotated datasets achieve performance comparable to fully manually annotated models. On COCO, the method reaches 40.5% AP, nearly matching the 40.9% AP of a fully annotated model while incurring only 1% of the labeling cost.
By Jonathan Klees, Timo Heller, Matthias Rottmann
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervision. However, these methods primarily improve supervision quality rather than explicitly enforcing semantic consistency in the learned representations of hard regions.
arXiv:2506. 11142v3 Announce Type: replace-cross Abstract: Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data, such as ineffective utilization of pseudo-labels, exacerbation of class imbalance biases, and neglect of prediction uncertainty.
By Ebenezer Tarubinga, Jenifer Kalafatovich, Seong-Whan Lee
The paper introduces Self‑Evolutionary CLIP (SE‑CLIP), a semi‑supervised framework that adapts vision‑language models like CLIP to satellite imagery. SE‑CLIP uses a two‑phase pipeline: an initial warm‑up on a small set of annotated seeds followed by a recursive discovery phase that iteratively selects high‑confidence samples from unlabeled data. A class‑balanced selection strategy is applied to keep the evolving support set balanced, and experiments on the UCM and NWPU benchmarks show that SE‑CLIP outperforms existing semi‑supervised methods.
By Mohamed L. Mekhalfi, Mohamad M. Al Rahhal, Yakoub Bazi, Salah E. Khenfer, Mingdeng Shi, Hua Zou, Mansour Zuair
arXiv:2507.22075v2 Announce Type: replace
Abstract: In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant nois...
By Eman Ali, Chetan Arora, Muhammad Haris Khan
The paper introduces SSS, a semi‑supervised framework that builds on the Vision Foundation Model SAM‑2 to improve medical image segmentation. It combines a weak‑to‑strong consistency regularization with a Discriminative Feature Enhancement mechanism and a prompt generator that uses Physical Constraints with a Sliding Window to supply prompts for unlabeled data. Experiments on the ACDC and BHSD datasets show that SSS outperforms prior methods, achieving a 53.15 Dice score on BHSD, a +3.65 improvement over the state of the art.
By Hongjie Zhu, Xiwei Liu, Rundong Xue, Zeyu Zhang, Yong Xu, Daji Ergu, Ying Cai, Yang Zhao
MixSIS introduces a mixed-supervised instance segmentation framework that permits a flexible combination of image, point, box, and mask annotations within a dataset, addressing the limitation of existing weakly-supervised methods that focus on a single annotation level. The authors present AnnsMixer, a baseline system that generates pseudo mask annotations from all annotation levels, even within a single image, and demonstrate its effectiveness on both WSIS and MixSIS tasks. Experiments and case studies show that MixSIS and AnnsMixer can reduce annotation time by up to 90% or improve segmentation results by 34% using simple annotation rules or heterogeneous data.
By Christian Wilms, Emre G\"ulsoylu, Sven Schreiber, Simone Frintrop
arXiv:2609.25850v1 Announce Type: new
Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
By Xiaofei Du, Lei Zhang, Shuyu Yan, Manning Wang, Zhijian Song
arXiv:2505.07675v3 Announce Type: replace-cross
Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...
By Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang