Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …

Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …

Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

Abstract Weakly-Supervised Concealed Object Segmentation (WSCOS) aims to segment
objects well blended with surrounding environments using sparsely-annotated data for …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in
their environment, which has various downstream applications in fields such as medicine …

Camouflaged object segmentation (COS) aims to identify objects that are" perfectly"
assimilate into their surroundings, which has a wide range of valuable applications. The key …

We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …

Spotting objects that are visually adapted to their surroundings is challenging for both
humans and AI. Conventional generic/salient object detection techniques are suboptimal for …

SAM is a segmentation model recently released by Meta AI Research and has been gaining
attention quickly due to its impressive performance in generic object segmentation …