activity
20182020
most citedJoint Disentangling and Adaptation for Cross-Domain Person Re-Identification

19 citations · 36 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CV2020

Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection

Zeyi Huang, Yang Zou, Vijayakumar Bhagavatula +1

Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to train object detectors using only the image-level category labels. However, without object-level label…

cs.CV2020

Hard Class Rectification for Domain Adaptation

Yunlong Zhang, Changxing Jing, Huangxing Lin +4

Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been wi…

cs.CV202019 cited

Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

Yang Zou, Xiaodong Yang, Zhiding Yu +2

Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge…

cs.CV201916 cited

Conservative Wasserstein Training for Pose Estimation

Xiaofeng Liu, Yang Zou, Tong Che +4

This paper targets the task with discrete and periodic class labels ( pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regres…

cs.CV20191 cited

Deep Classification Network for Monocular Depth Estimation

Azeez Oluwafemi, Yang Zou, B. V. K. Vijaya Kumar

Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentall…

cs.CV2019

Confidence Regularized Self-Training

Yang Zou, Zhiding Yu, Xiaofeng Liu +2

Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of…