arXiv:2607. 12704v1 Announce Type: cross Abstract: Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training.
By Alaa Almouradi, Erchan Aptoula
The paper introduces a semi‑supervised hyperspectral image classification framework that combines spatial prior information with a dynamic learning mechanism. It proposes an Edge‑Aware Superpixel Label Propagation module to reduce boundary label diffusion and a Dynamic History‑Fused Prediction method to stabilize pseudo‑labels over time. Additionally, an Adaptive Tripartite Sample Categorization strategy is used to hierarchically exploit easy, ambiguous, and hard samples, resulting in improved pseudo‑label quality and learning efficiency. The combined Dynamic Reliability‑Enhanced Pseudo‑Label Framework achieves spatio‑temporal consistency optimization and demonstrates superior performance on four benchmark datasets.
By Yunfei Qiu, Qiqiong Ma, Tianhua Lv, Li Fang, Shudong Zhou, Wei Yao
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
By Wenxiao Fan, Kan Li
arXiv:2603.00604v2 Announce Type: replace
Abstract: High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to o...
By Keiller Nogueira, Codrut-Andrei Diaconu, D\'avid Kerekes, Jakob Gawlikowski, C\'edric L\'eonard, Nassim Ait Ali Braham, June Moh Goo, Zichao Zeng, Zhipeng Liu, Pallavi Jain, Andrea Nascetti, Ronny H\"ansch
arXiv:2606. 08718v1 Announce Type: cross Abstract: While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors.
By Md Abdullah Al Forhad, Weishi Shi
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