  Camouflaged object detection (COD) approaches heavily rely on pixel-level
annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse
annotations like scribbles or points to reduce annotation effort, but this can
lead to decreased accuracy. 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.
Additionally, it only provides localization information instead of semantic
one, which can intrinsically cause ambiguity in interpreting the targets. 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. To that end, we
introduce a test-time adaptation per-instance mechanism called Generalizable
SAM (GenSAM) to automatically enerate and optimize visual prompts the generic
task prompt for WSCOD. In particular, 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. our codes is in: https://lwpyh.github.io/GenSAM/.
