207 citations · 985 across the 15 of their papers we have counts for
15 papers
Rethinking Domain Generalization: Discriminability and Generalizability
Shaocong Long, Qianyu Zhou, Chenhao Ying +2
Domain generalization(DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques te…
Diverse Target and Contribution Scheduling for Domain Generalization
Shaocong Long, Qianyu Zhou, Chenhao Ying +2
Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets…
Instance-Aware Domain Generalization for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +4
Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain…
Rethinking Implicit Neural Representations for Vision Learners
Yiran Song, Qianyu Zhou, Lizhuang Ma
Implicit Neural Representations (INRs) are powerful to parameterize continuous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., im…
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +3
With various face presentation attacks emerging continually, face anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based…
Generative Domain Adaptation for Face Anti-Spoofing
Qianyu Zhou, Ke-Yue Zhang, Taiping Yao +4
Face anti-spoofing (FAS) approaches based on unsupervised domain adaption (UDA) have drawn growing attention due to promising performances for target scenarios. Most existing UDA F…
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
Qianyu Zhou, Xiangtai Li, Lu He +5
Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance…
Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
Qianyu Zhou, Chuyun Zhuang, Ran Yi +2
Unsupervised domain adaptation (UDA) for semantic segmentation has been well-studied in recent years. However, most existing works largely neglect the local regional consistency ac…
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Qiqi Gu, Qianyu Zhou, Minghao Xu +5
Cross-domain object detection and semantic segmentation have witnessed impressive progress recently. Existing approaches mainly consider the domain shift resulting from external en…
Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling
Hongyi Xu, Fengqi Liu, Qianyu Zhou +4
3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially…
Context-Aware Mixup for Domain Adaptive Semantic Segmentation
Qianyu Zhou, Zhengyang Feng, Qiqi Gu +5
Unsupervised domain adaptation (UDA) aims to adapt a model of the labeled source domain to an unlabeled target domain. Existing UDA-based semantic segmentation approaches always re…
Self-Adversarial Disentangling for Specific Domain Adaptation
Qianyu Zhou, Qiqi Gu, Jiangmiao Pang +2
Domain adaptation aims to bridge the domain shifts between the source and the target domain. These shifts may span different dimensions such as fog, rainfall, etc. However, recent…
End-to-End Video Object Detection with Spatial-Temporal Transformers
Lu He, Qianyu Zhou, Xiangtai Li +7
Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous c…
Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation
Qianyu Zhou, Zhengyang Feng, Qiqi Gu +4
Unsupervised domain adaptation (UDA) aims to adapt existing models of the source domain to a new target domain with only unlabeled data. Most existing methods suffer from noticeabl…
DMT: Dynamic Mutual Training for Semi-Supervised Learning
Zhengyang Feng, Qianyu Zhou, Qiqi Gu +5
Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Se…