activity
20162023
most citedBoundary-Guided Camouflaged Object Detection

322 citations · 360 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2023★ 1 cited

A Unified Query-based Paradigm for Camouflaged Instance Segmentation

Bo Dong, Jialun Pei, Rongrong Gao +3

Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization…

cs.CV2023★ 2 cited

Diffusion Model for Camouflaged Object Detection

Zhennan Chen, Rongrong Gao, Tian-Zhu Xiang +1

Camouflaged object detection is a challenging task that aims to identify objects that are highly similar to their background. Due to the powerful noise-to-image denoising capabilit…

cs.CV2023★ 2 cited

CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation

Lizhao Liu, Zhuangwei Zhuang, Shangxin Huang +5

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of d…

cs.CV2023★ 9 cited

Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers

Zhou Huang, Hang Dai, Tian-Zhu Xiang +4

Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective…

cs.CV2023★ 1 cited

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

Peng Zheng, Jie Qin, Shuo Wang +2

Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for f…

cs.CV2022★ 16 cited

Trichomonas Vaginalis Segmentation in Microscope Images

Lin Li, Jingyi Liu, Shuo Wang +2

Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated. Auto…