Camouflaged Object Segmentation with a Single Task-generic Prompt

2 papers with code • 3 benchmarks • 2 datasets

The previous fully-supervised and weakly-supervised camouflaged object segmentation tasks required a significant amount of annotated data for supervised training to enable models to segment camouflaged objects effectively. However, models like the Segment Anything Model (SAM), which falls under the category of Promptable Segmentation models, can achieve excellent segmentation performance on unseen images with just an instance-specific visual prompt. Nevertheless, for complex scenarios like camouflaged objects, SAM may not perform well even with an instance-specific prompt. Furthermore, the question arises: Is an instance-specific prompt necessary? In more realistic scenarios, where only a task-generic task description is provided as a universally applicable text prompt, how can we improve segmentation across various datasets under the camouflaged object segmentation task?

Most implemented papers

Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation

lwpyh/ProMaC_code 27 Aug 2024

In this paper, we utilize hallucinations to mine task-related information from images and verify its accuracy for enhancing precision of the generated prompts.