7 accepted papers
Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segm…
Vision-Language models (e.g., CLIP) facilitate the development of open-vocabulary camouflaged object segmentation (OVCOS), but existing methods still rely on mask annotations for fully-supervised training. In contrast, the training-free paradigm can rapidly process unseen data, representing a highly
Matrix graph signals, which are associated with two factor graphs, are ubiquitous in daily life, such as time-varying physical signals in sensor networks and rating matrices in recommendation systems. In practice, due to the row-wise and column-wise smoothness, they are modeled as bandlimited (BL) g…
Open-Vocabulary Camouflaged Object Segmentation (OVCOS) aims to segment camouflaged objects of any category based on text descriptions. Despite existing open-vocabulary methods exhibit strong segmentation capabilities, they still have a major limitation in camouflaged scenarios: semantic confusion,…
Vision transformers (ViTs) perform exceptionally well in various computer vision tasks but remain vulnerable to adversarial attacks. Recent studies have shown that the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs. However, existing ViT attacks aggressivel…
Underwater transparently camouflaged organisms can be perfectly “invisible” in the ocean to avoid the capture of predators. Due to the blurry contour boundaries of their bodies, obtaining their boundary features and determining their specific positions are challenging for detection tasks. To address
Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number o…