activity
20222024
most citedAdaptive Context Selection for Polyp Segmentation

12 citations · 17 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Hybrid 3D Human Pose Estimation with Monocular Video and Sparse IMUs

Yiming Bao, Xu Zhao, Dahong Qian

Temporal 3D human pose estimation from monocular videos is a challenging task in human-centered computer vision due to the depth ambiguity of 2D-to-3D lifting. To improve accuracy…

cs.CV20235 cited

Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification

Suncheng Xiang, Jingsheng Gao, Mengyuan Guan +5

Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision, which plays a significant role in realistic scenarios due to it…

cs.CV2023

Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval

Qingzhong Chen, Shilun Cai, Crystal Cai +3

Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval…

cs.CV202312 cited

Adaptive Context Selection for Polyp Segmentation

Ruifei Zhang, Guanbin Li, Zhen Li +3

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and…

cs.CV2022

FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation

Yiming Bao, Xu Zhao, Dahong Qian

There exist challenging problems in 3D human pose estimation mission, such as poor performance caused by occlusion and self-occlusion. Recently, IMU-vision sensor fusion is regarde…

cs.CV2022

SubFace: Learning with Softmax Approximation for Face Recognition

Hongwei Xu, Suncheng Xiang, Dahong Qian

The softmax-based loss functions and its variants (e.g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes. A co…