HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
By Li Pang, Xinqiao Wu, Jing Yao, Pedram Ghamisi, Jun Zhou, Zhengchao Chen, Deyu Meng, Xiangyong Cao
arXiv:2601. 08446v2 Announce Type: replace-cross Abstract: The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS).
By Tom Burgert, Julia Henkel, Beg\"um Demir
Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a general-purpose hyperspectral foundation model...
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
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
The paper introduces a two-stage framework for point-supervised change detection that leverages SAM2 priors to generate object-aware candidate masks and refines them with a lightweight CNN and uncertainty-aware loss. In the second stage, a teacher‑student self‑training loop with exponential moving average updates continuously improves pseudo‑labels and model performance. Experiments on WHU-CD, LEVIR-CD, and SYSU-CD show the method surpasses prior weakly supervised approaches and competes with fully supervised ones.
By Hailong Ning, Hao Wang, Yimeng Wang, Tao Lei, Renwei Dian, Asoke K. Nandi
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
HyperVision introduces the first ground‑based hyperspectral pre‑trained backbone, addressing challenges of varying spectral configurations, limited annotations, and dataset diversity. It employs a channel‑adaptive dynamic embedding to unify heterogeneous inputs, a multi‑source pseudo‑labeling strategy combining SAM2 spatial cues with HyperFree spectral details, and cross‑modal knowledge distillation from a pre‑trained RGB vision model. Trained on 15k images from 26 datasets, HyperVision achieves significant improvements—up to 16.3% relative gain in hyperspectral semantic segmentation, 2.1% in object tracking AUC, and 35.5% reduction in salient object detection MAE—while requiring only head‑only adaptation.
By Guanyiman Fu, Jingtao Li, Zihang Cheng, Zhuanfeng Li, Diqi Chen, Yan Xu, Xiangyu Liu, Fengchao Xiong, Jianfeng Lu, Chengrong Chen, Jun Zhou
The paper presents a semi‑supervised biomedical image segmentation method that uses a diffusion‑based teacher–student framework. The teacher is pretrained via unsupervised diffusion reconstruction and then co‑trained with a student, leveraging supervised labels and cross pseudo‑supervision on unlabeled data. A multi‑round extension generates multiple stochastic reconstructions to further refine pseudo‑labels, achieving competitive or superior results on several 2D and 3D biomedical datasets, especially when labels are scarce.
By Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi
arXiv:2606. 16868v1 Announce Type: cross Abstract: While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site label imperfections such as contour disagreement, missing or additional structures, and confused labels.
By Markus Bujotzek, Dimitrios Bounias, Stefan Denner, Ralf Floca, Maximilian Fischer, Peter Neher, Klaus Maier-Hein
The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.
By Liqian Yang, Xingchi Chen, Xinfeng Gui, Xiangyong Cao, Qianxin Yi
The paper introduces a two‑stage framework for point‑supervised change detection that leverages SAM2 priors to generate object‑aware candidate masks from sparse point annotations. In Stage I, a mask selection strategy converts generic segmentation outputs into reliable change pseudo‑labels, followed by a lightweight CNN refinement module with an uncertainty‑aware loss to enhance boundary quality. Stage II employs a teacher‑student self‑training loop, where the teacher is updated via exponential moving average and periodically refreshes pseudo‑labels, creating a closed‑loop optimization that alternates between pseudo‑label refinement and model re‑optimization. Experiments on WHU‑CD, LEVIR‑CD, and SYSU‑CD show the method surpasses prior weakly supervised approaches and competes with several fully supervised methods.