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Siyuan Qiao

31 papers hereh-index 245.8k citations50 works total

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

author position
  • first author7
  • middle author16
  • last author1

Across the 24 of 31 papers where every author was matched, so the position is known.

fields
  • cs.CV30
  • cs.CL1
same name
  • Siyuan Qiao — 6 papers, h 3
  • Siyuan Qiao — 5 papers, h 8
  • Siyuan Qiao — 3 papers, h 2
  • Siyuan Qiao — 2 papers
  • Siyuan Qiao — 2 papers, h 2
  • Siyuan Qiao — 1 paper, h 0

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
20172023
most citedPaLM 2 Technical Report

168 citations · 284 across the 14 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019

Rethinking Normalization and Elimination Singularity in Neural Networks

Siyuan Qiao, Huiyu Wang, Chenxi Liu +2

In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the trainin…

cs.CV2019

Deeply Shape-guided Cascade for Instance Segmentation

Hao Ding, Siyuan Qiao, Alan Yuille +1

The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across mul…

cs.CV2019

TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion

Mingqing Xiao, Adam Kortylewski, Ruihai Wu +3

Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency…

cs.CV2019

Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

Siyuan Qiao, Huiyu Wang, Chenxi Liu +2

Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typical…

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