Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable
of recognizing objects from both base and novel categories. Recent advances leverage …

Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Cross-domain (CD) hyperspectral image classification (HSIC) has been significantly
boosted by methods employing Few-Shot Learning (FSL) based on CNNs or GCNs …

Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain
distributions, with a small number of target labels available, achieving better classification …

Most domain adaptation (DA) methods are based on either a convolutional neural networks
(CNNs) or a vision transformers (ViTs). They align the distribution differences between …

Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via
collaborative distributed learning by aggregating multiple client models trained with local …

Domain Adaptation (DA) is used to reduce cross-domain differences between the labeled
source and unlabeled target domains. As the existing semantic-based DA approaches …

Partial label learning (PLL) seeks a classification model with partially labeled (PL) instances.
Each PL instance is attached with a candidate label set, with only one being ground-truth. A …

This paper presents a semi-supervised change detection optimization strategy as a means
to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The …

The enhanced representational power and broad applicability of deep learning models have
attracted significant interest from the research community in recent years. However, these …