November 2026

Open-Vocabulary Pedestrian Attribute Recognition (OV-PAR) aims to recognize and understand novel attributes that do not appear in the training set. Although much progress has been made in existing ope

In low-light video capture, long exposure times increase intra-frame image blur, and hold-type displays introduce motion-dependent display blur during playback. Video stabilization reduces inter-frame

Ship detection from remote sensing imagery constitutes a cornerstone for applications such as marine monitoring and maritime traffic management. However, existing methods based on horizontal bounding

Vision foundation models have recently emerged as a powerful paradigm for visual understanding by learning transferable representations from large-scale unlabeled data. While self-supervised vision tr

Anomaly detection (AD) in remote sensing aims to identify pixels or regions that deviate from the normal background distribution, without relying on any labeled anomalies. Many unsupervised anomaly de

Domain adaptive object detection (DAOD) methods have demonstrated promising performance in detecting objects in unseen scenarios by transferring knowledge from a labeled source domain to an unlabeled

Compositional Zero-Shot Learning (CZSL) recognizes unseen attribute-object compositions by transferring primitive knowledge learned from seen pairs. Existing CLIP-based approaches often rely on simple

Brain decoding aims to translate neural signals into meaningful information, advancing brain-computer interfaces and cognitive neuroscience theories. Existing methods typically focus on signals from t

360-degree videos (abbreviated as 360 videos) have gained widespread popularity due to their immersive experience. The massive amount of raw data resulting from ultra-high resolution 360 videos makes

Neural network-based in-loop filtering (ILF) technologies have achieved remarkable coding gains, emerging as a promising candidate for next-generation video coding standards. However, using deep neura