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researcher

Xiaofei He

6 papers here

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

author position
  • middle author4
  • last author1

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.IR1
  • cs.LG1
ORCID 0009-0001-9107-2354
same name
  • Xiaofei He — 36 papers, h 83
  • Xiaofei He — 14 papers, h 9
  • Xiaofei He — 9 papers, h 5
  • Xiaofei He — 1 paper, h 2

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
20142022
most citedGeodesic Distance Function Learning via Heat Flow on Vector Fields

6 citations · 11 across the 6 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CV2022

Towards In-distribution Compatibility in Out-of-distribution Detection

Boxi Wu, Jie Jiang, Haidong Ren +7

Deep neural network, despite its remarkable capability of discriminating targeted in-distribution samples, shows poor performance on detecting anomalous out-of-distribution data. T…

cs.IR2022

CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation

Shengyu Zhang, Bofang Li, Dong Yao +7

Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…

cs.CV2022★ 3 cited

Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph

Honghui Yang, Zili Liu, Xiaopei Wu +4

Two-stage detectors have gained much popularity in 3D object detection. Most two-stage 3D detectors utilize grid points, voxel grids, or sampled keypoints for RoI feature extractio…

cs.CV2022★ 2 cited

Towards Efficient Adversarial Training on Vision Transformers

Boxi Wu, Jindong Gu, Zhifeng Li +3

Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention. Recent work showed that ViTs are also vulnerable to adversar…

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