13 citations · 38 across the 19 of their papers we have counts for
9 papers · 1 filter
Bootstrap Masked Visual Modeling via Hard Patches Mining
Haochen Wang, Junsong Fan, Yuxi Wang +4
Masked visual modeling has attracted much attention due to its promising potential in learning generalizable representations. Typical approaches urge models to predict specific con…
Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation
Yuxi Wang, Jian Liang, Jun Xiao +3
Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These…
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
Haochen Wang, Junsong Fan, Yuxi Wang +3
As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enha…
TDG: Text-guided Domain Generalization
Geng Liu, Yuxi Wang
Domain generalization (DG) attempts to generalize a model trained on single or multiple source domains to the unseen target domain. Benefiting from the success of Visual-and-Langua…
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
Jingfan Chen, Yuxi Wang, Pengfei Wang +4
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes. Previous work has introduced generative…
Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
Haochen Wang, Yuchao Wang, Yujun Shen +3
The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to s…
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation
Haochen Wang, Yujun Shen, Jingjing Fei +4
Domain adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, existing methods primarily focus on directly l…
Hard Patches Mining for Masked Image Modeling
Haochen Wang, Kaiyou Song, Junsong Fan +3
Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually…
A Survey of Deep Visual Cross-Domain Few-Shot Learning
Wenjian Wang, Lijuan Duan, Yuxi Wang +3
Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have…