activity
20162023
most citedFully Sparse 3D Object Detection

41 citations · 291 across the 43 of their papers we have counts for

collaborators

59 papers

cs.CV2023★ 2 cited

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…

cs.CV2023★ 4 cited

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…

cs.CV2023★ 3 cited

PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation

Yuqi Wang, Yuntao Chen, Xingyu Liao +2

Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentatio…

cs.CV2023

Weakly Supervised 3D Object Detection with Multi-Stage Generalization

Jiawei He, Yuqi Wang, Yuntao Chen +1

With the rapid development of large models, the need for data has become increasingly crucial. Especially in 3D object detection, costly manual annotations have hindered further ad…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 1 cited

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…