39 citations · 163 across the 9 of their papers we have counts for
12 papers
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Yongwei Chen, Rui Chen, Jiabao Lei +2
Creation of 3D content by stylization is a promising yet challenging problem in computer vision and graphics research. In this work, we focus on stylizing photorealistic appearance…
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Yabin Zhang, Minghan Li, Ruihuang Li +2
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. Wit…
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Ruihuang Li, Shuai Li, Chenhang He +3
Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…
Gradual Domain Adaptation via Self-Training of Auxiliary Models
Yabin Zhang, Bin Deng, Kui Jia +1
Domain adaptation becomes more challenging with increasing gaps between source and target domains. Motivated from an empirical analysis on the reliability of labeled source data fo…
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Yabin Zhang, Haojian Zhang, Bin Deng +3
Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn eff…
On Universal Black-Box Domain Adaptation
Bin Deng, Yabin Zhang, Hui Tang +2
In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the…