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).
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.
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.
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.
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...
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.
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.
arXiv:2606. 11695v1 Announce Type: cross Abstract: High-quality labeled data is essential for training reliable ML/DL models.
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.
The paper proposes a method that first pre‑trains a feature extractor on the target dataset using in‑domain self‑supervised learning (SSL) without labels, then performs standard supervised training on the same noisy dataset. This two‑stage approach eliminates the need for a clean label subset and consistently improves classification accuracy and label‑error detection across synthetic and real‑world noise, especially as noise rates increase. Experiments show that the method matches or surpasses ImageNet and DinoV2 pre‑training, particularly under high noise conditions.
arXiv:2609.26631v1 Announce Type: new Abstract: Accurate ground-based cloud classification is important for atmospheric monitoring, solar-energy forecasting, aviation weather assessment, and climate...
arXiv:2606. 07086v1 Announce Type: cross Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets.
arXiv:2508. 09697v4 Announce Type: replace Abstract: Noisy labels are inevitable in real-world multimedia applications.