13 citations · 38 across the 19 of their papers we have counts for
20 papers
PUGS: Physics-Guided Uncertainty Modeling for Underwater Dynamic 3D Gaussian Splatting
Tingjun Li, Yuxi Wang, Guoqiang Zhong +1
Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics with deterministic deformation…
Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion
Yuqiang Huang, Yuxi Wang, Junyu Dong +1
Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variations. Most existing Methods are…
Enhancing Sound Source Localization via False Negative Elimination
Zengjie Song, Jiangshe Zhang, Yuxi Wang +2
Sound source localization aims to localize objects emitting the sound in visual scenes. Recent works obtaining impressive results typically rely on contrastive learning. However, t…
CityX: Controllable Procedural Content Generation for Unbounded 3D Cities
Shougao Zhang, Mengqi Zhou, Yuxi Wang +5
Urban areas, as the primary human habitat in modern civilization, accommodate a broad spectrum of social activities. With the surge of embodied intelligence, recent years have witn…
MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets
Zeyu Li, Ruitong Gan, Chuanchen Luo +7
Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance. By performing score distillation sampl…
StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework
Yiheng Huang, Hui Yang, Chuanchen Luo +5
Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ dispa…
Continual Forgetting for Pre-trained Vision Models
Hongbo Zhao, Bolin Ni, Haochen Wang +6
For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests ori…
SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition
Xu Hu, Yuxi Wang, Lue Fan +6
3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis, benefiting from its high-quality rendering results and real-time rendering speed. Ho…
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
Genghao Zhang, Yuxi Wang, Chuanchen Luo +4
Indoor scene generation has attracted significant attention recently as it is crucial for applications of gaming, virtual reality, and interior design. Current indoor scene generat…
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…
Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes
Zengjie Song, Yuxi Wang, Junsong Fan +2
Sound source localization in visual scenes aims to localize objects emitting the sound in a given image. Recent works showing impressive localization performance typically rely on…
A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation
Yuxi Wang, Jian Liang, Zhaoxiang Zhang
Source-free domain adaptation has developed rapidly in recent years, where the well-trained source model is adapted to the target domain instead of the source data, offering the po…