3k citations · 3.3k across the 27 of their papers we have counts for
32 papers
Progressive End-to-End Object Detection in Crowded Scenes
Anlin Zheng, Yuang Zhang, Xiangyu Zhang +2
In this paper, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be i…
Focal Sparse Convolutional Networks for 3D Object Detection
Yukang Chen, Yanwei Li, Xiangyu Zhang +2
Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic co…
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Ziwei Luo, Youwei Li, Shen Cheng +6
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-…
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
Guocheng Qian, Xuanyang Zhang, Guohao Li +5
The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which i…
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Yisheng He, Haoqiang Fan, Haibin Huang +2
In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstra…