18 citations · 21 across the 10 of their papers we have counts for
10 papers
Synthetic-to-Real Translation for Class-Agnostic Motion Prediction
Yizheng Wu, Hongwei Fan, Kewei Wang +8
Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A key challenge in this domain…
iControl3D: An Interactive System for Controllable 3D Scene Generation
Xingyi Li, Yizheng Wu, Jun Cen +6
3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D…
Pseudo-Labeling by Multi-Policy Viewfinder Network for Image Cropping
Zhiyu Pan, Kewei Wang, Yizheng Wu +4
Automatic image cropping models predict reframing boxes to enhance image aesthetics. Yet, the scarcity of labeled data hinders the progress of this task. To overcome this limitatio…
Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation
Yizheng Wu, Zhiyu Pan, Kewei Wang +5
Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3…
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
Xingyi Li, Zhiguo Cao, Yizheng Wu +4
Current 3D stylization methods often assume static scenes, which violates the dynamic nature of our real world. To address this limitation, we present S-DyRF, a reference-based spa…
Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations
Kewei Wang, Yizheng Wu, Jun Cen +5
The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems, wherein class-agnostic motion prediction methods directly pr…
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
Kewei Wang, Yizheng Wu, Zhiyu Pan +5
Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-perform…
SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang +6
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while…
SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing
Jiwei Chen, Kewei Wang, Wen Su +1
Crowd counting based on density maps is generally regarded as a regression task.Deep learning is used to learn the mapping between image content and crowd density distribution. Alt…
On Exploring and Improving Robustness of Scene Text Detection Models
Shilian Wu, Wei Zhai, Yongrui Li +2
It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. Fo…