5 papers · 1 filter
Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels
Yimin Fu, Songbo Wang, Lizhuo Liu +3
Existing data-driven infrared small target detection (ISTD) methods typically require large-scale datasets with accurate pixel-level annotations for model training. However, such l…
GeoThreat: Transferable Targeted Adversarial Attacks on Large Vision-Language Models for Remote Sensing Image Interpretation
Yimin Fu, Yuefeng Bai, Baicheng Pan +2
Adversarial attacks against large vision-language models (LVLMs) serve as an effective means of assessing their robustness in cross-modal semantic understanding. Existing studies m…
Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain
Yimin Fu, Songbo Wang, Feiyan Wu +3
The accurate target-background separation in infrared small target detection (IRSTD) highly depends on the discriminability of extracted representations. However, most existing met…
Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition
Runqing Yang, Yimin Fu, Changyuan Wu +1
Existing open set recognition (OSR) methods are typically designed for static scenarios, where models aim to classify known classes and identify unknown ones within fixed scopes. T…
Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labels
Yimin Fu, Zhunga Liu, Dongxiu Guo +1
The acquisition of high-quality labeled synthetic aperture radar (SAR) data is challenging due to the demanding requirement for expert knowledge. Consequently, the presence of unre…