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Shuai Li

14 papers hereh-index 16961 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author11

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CV13
  • cs.LG1
same name
  • Shuai Li — 32 papers, h 17
  • Shuai Li — 31 papers, h 21
  • Shuai Li — 15 papers, h 3
  • Shuai Li — 13 papers, h 11
  • Shuai Li — 9 papers, h 27
  • Shuai Li — 8 papers, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212025
most citedSemi-supervised Models are Strong Unsupervised Domain Adaptation Learners

15 citations · 46 across the 13 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022★ 12 cited

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds

Chenhang He, Ruihuang Li, Shuai Li +1

Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cl…

cs.CV2022★ 1 cited

Towards Robust 2D Convolution for Reliable Visual Recognition

Lida Li, Shuai Li, Kun Wang +2

2D convolution (Conv2d), which is responsible for extracting features from the input image, is one of the key modules of a convolutional neural network (CNN). However, Conv2d is vu…

cs.CV2022★ 1 cited

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation

Ruihuang Li, Shuai Li, Chenhang He +3

Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…

cs.CV2022★ 5 cited

A Dual Weighting Label Assignment Scheme for Object Detection

Shuai Li, Chenhang He, Ruihuang Li +1

Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.