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:2609.23019v1 Announce Type: cross
Abstract: Soma instance segmentation, i.e., identifying and delineating individual cell somas as distinct instances, is crucial for cellular analysis and conne...
By Mohammad Khateri, Morteza Ghahremani, Jussi Tohka, Alejandra Sierra
arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.
By Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao
arXiv:2608.30003v1 Announce Type: new
Abstract: Prototypical part-based models provide explainable predictions by comparing input regions to learned prototypes. However, current approaches are burden...
By Il\'an Carretero, Gustavo Jes\'us Angulo, Roc\'io del Amor, Valery Naranjo
arXiv:2608.21136v1 Announce Type: new
Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
By Jie Xu, Na Zhao
UVU is a vision-language unified autoregressive framework that integrates visual supervision directly into the pre-training stage of multimodal large language models. By using continuous visual encoding and a large-scale iterative hierarchical clustering algorithm to build a pixel-level visual codebook, UVU enables lossless representation of visual inputs and autoregressive generation of pixel-level image tokens alongside textual tokens. This approach synergizes pixel-level visual perception with semantic-level visual understanding, allowing models to internalize visual reconstruction capabilities and improve multimodal understanding performance.
By Zhehan Kan, Xinghua Jiang, Yubo Zhu, Yanlin Liu, Xiaochen Yang, Zhixiang Wei, Shifeng Liu, Qingmin Liao, Wenming Yang, Xin Li, Yinsong Liu, Deqiang Jiang, Xing Sun
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
arXiv:2608.22679v1 Announce Type: new
Abstract: Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as...
By Changki Sung, Hyungtae Lim, Wanhee Kim, Youngwoo Seo, Hyun Myung
arXiv:2607.12896v3 Announce Type: replace
Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
By Yunzhou Li, Jiesi Hu, Yanwu Yang, Hanyang Peng, Chenfei Ye, Jianfeng Cao, Yixuan Yuan, Ting Ma
arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.
By Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang
arXiv:2608.21443v1 Announce Type: new
Abstract: Estimating interpretable conditional-dependence structures from multimodal visual-linguistic features remains largely unexplored. We propose CM-GLasso...
By Fei Wang, Yutong Zhang, Yang Ye, Jinxian Chen, Wang Wenshuai, Xiong Wang
arXiv:2608. 07340v1 Announce Type: cross Abstract: Registration-based Few-shot medical image segmentation (RFMIS) aims to generate pseudo-labels for unlabeled images by warping a labeled image through registration.
By Jia Wang, Jiaming Cai, Zunying Hu, Zhanjie Wu, Jinyuan Liu, Hua Cheng, Yun Peng