3 papers
cs.CV2025
SAM-TTT: Segment Anything Model via Reverse Parameter Configuration and Test-Time Training for Camouflaged Object Detection
Zhenni Yu, Li Zhao, Guobao Xiao +1
This paper introduces a new Segment Anything Model (SAM) that leverages reverse parameter configuration and test-time training to enhance its performance on Camouflaged Object Dete…
cs.CV2024
COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection
Xiaoqin Zhang, Zhenni Yu, Li Zhao +2
We rethink the segment anything model (SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection (COD). SAM has zero-shot generalization abil…
cs.CV2024
Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection
Zhenni Yu, Xiaoqin Zhang, Li Zhao +2
This paper introduces a new Segment Anything Model with Depth Perception (DSAM) for Camouflaged Object Detection (COD). DSAM exploits the zero-shot capability of SAM to realize pre…