149 citations · 447 across the 23 of their papers we have counts for
5 papers · 1 filter
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
Yuqi Lin, Minghao Chen, Wenxiao Wang +5
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training cos…
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
Xiaopei Wu, Yang Zhao, Liang Peng +6
Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D obj…
Convolutional Embedding Makes Hierarchical Vision Transformer Stronger
Cong Wang, Hongmin Xu, Xiong Zhang +3
Vision Transformers (ViTs) have recently dominated a range of computer vision tasks, yet it suffers from low training data efficiency and inferior local semantic representation cap…
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Liang Peng, Xiaopei Wu, Zheng Yang +2
Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. T…
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Xiaopei Wu, Liang Peng, Honghui Yang +5
Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different represen…