2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Scalable and Generalizable Correspondence Pruning via Geometry-Consistent Pre-training
Tangfei Liao, Xiaoqin Zhang, Tao Wang +4
Two-view correspondence pruning aims to identify reliable correspondences for camera pose estimation, serving as a fundamental step in many 3D vision tasks. Existing methods rely o…
VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning
Tangfei Liao, Xiaoqin Zhang, Li Zhao +2
Correspondence pruning aims to find correct matches (inliers) from an initial set of putative correspondences, which is a fundamental task for many applications. The process of fin…