11 citations · 18 across the 5 of their papers we have counts for
7 papers
Voxel Transformer for 3D Object Detection
Jiageng Mao, Yujing Xue, Minzhe Niu +5
We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in vo…
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
Jiageng Mao, Minzhe Niu, Haoyue Bai +3
We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds. Current approaches generally rely on the points or v…
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
Haoyue Bai, Fengwei Zhou, Lanqing Hong +3
Recent advances on Out-of-Distribution (OoD) generalization reveal the robustness of deep learning models against distribution shifts. However, existing works focus on OoD algorith…
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification
Haoyue Bai, Song Wen, S. -H. Gary Chan
Labeled crowd scene images are expensive and scarce. To significantly reduce the requirement of the labeled images, we propose ColorCount, a novel CNN-based approach by combining s…
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting
Haoyue Bai, S. -H. Gary Chan
We study video crowd counting, which is to estimate the number of objects (people in this paper) in all the frames of a video sequence. Previous work on crowd counting is mostly on…
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…