3 papers
cs.CV2026
Temporal-Emerged Prompting for Segment Anything in Multiframe Infrared Small Target Detection
Yinghui Xing, Donghao Chu, Shizhou Zhang +1
Accurately localizing and segmenting small targets in low signal-to-noise ratio (SNR) infrared sequences remains a challenging task. Since targets are often indistinguishable from…
cs.CV2026
Better Matching, Less Forgetting: A Quality-Guided Matcher for Transformer-based Incremental Object Detection
Qirui Wu, Shizhou Zhang, De Cheng +4
Incremental Object Detection (IOD) aims to continuously learn new object classes without forgetting previously learned ones. A persistent challenge is catastrophic forgetting, prim…
cs.CV2026
Attention Retention for Continual Learning with Vision Transformers
Yue Lu, Xiangyu Zhou, Shizhou Zhang +3
Continual learning (CL) empowers AI systems to progressively acquire knowledge from non-stationary data streams. However, catastrophic forgetting remains a critical challenge. In t…