most citedFocal Sparse Convolutional Networks for 3D Object Detection

16 citations · 22 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2022

Voxel Field Fusion for 3D Object Detection

Yanwei Li, Xiaojuan Qi, Yukang Chen +4

In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cros…

cs.CV2022

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…

cs.CV202216 cited

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…

math.OC20221 cited

Distributed stochastic projection-free solver for constrained optimization

Xia Jiang, Xianlin Zeng, Lihua Xie +2

This paper proposes a distributed stochastic projection-free algorithm for large-scale constrained finite-sum optimization whose constraint set is complicated such that the project…

cs.CV2022

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-…

cs.AI2022

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…