Camouflaged Object Segmentation

UNet++ is an architecture for semantic segmentation based on the U-Net. Through the use of densely connected nested decoder sub-networks, it enhances extracted feature processing and was reported by its authors to outperform the U-Net in Electron Microscopy (EM), Cell, Nuclei, Brain Tumor, Liver and Lung Nodule medical image segmentation tasks.

BASNet, or Boundary-Aware Segmentation Network, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.

The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. However, manual prompt is not always feasible, as it may not be accessible in real-world application. In this work, we aim to eliminate the need for manual prompt.The key idea is to employ Cross-modal Chains of Thought Prompting (CCTP) to reason visual prompts using the semantic information given by a generic text prompt. We introduce a test-time adaptation per-instance mechanism called Generalizable SAM (GenSAM) to automatically generate and optimize visual prompts the generic task prompt. CCTP maps a single generic text prompt onto image-specific consensus foreground and background heatmaps using vision-language models, acquiring reliable visual prompts. Moreover, to test-time adapt the visual prompts, we further propose Progressive Mask Generation (PMG) to iteratively reweight the input image, guiding the model to focus on the targets in a coarse-to-fine manner.Crucially, all network parameters are fixed, avoiding the need for additional training.Experiments demonstrate the superiority of GenSAM. Experiments on three benchmarks demonstrate that GenSAM outperforms point supervision approaches and achieves comparable results to scribble supervision ones, solely relying on general task descriptions as prompts.