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Haoran Wang

Xidian University

5 papers hereh-index 11407 citations15 works total

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

author position
  • first author1
  • middle author3
  • last author1

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

fields
  • cs.CV4
  • eess.IV1
affiliations
  • Xidian University
same name
  • Haoran Wang — 17 papers, h 11
  • Haoran Wang — 10 papers, h 15
  • Haoran Wang — 10 papers, h 6
  • Haoran Wang — 10 papers, h 5
  • Haoran Wang — 9 papers, h 12
  • Haoran Wang — 8 papers, h 3

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
20182023
most citedParameter-Free Spatial Attention Network for Person Re-Identification

78 citations · 87 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023

Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning

Yeming Chen, Siyu Zhang, Yaoru Sun +2

With the success of self-supervised learning, multimodal foundation models have rapidly adapted a wide range of downstream tasks driven by vision and language (VL) pretraining. Sta…

cs.CV2023★ 2 cited

S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields

Zeke Xie, Xindi Yang, Yujie Yang +5

Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images. Ne…

cs.CV2022★ 2 cited

Temporal Saliency Query Network for Efficient Video Recognition

Boyang Xia, Zhihao Wang, Wenhao Wu +2

Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient f…

cs.CV2018★ 78 cited

Parameter-Free Spatial Attention Network for Person Re-Identification

Haoran Wang, Yue Fan, Zexin Wang +2

Global average pooling (GAP) allows to localize discriminative information for recognition [40]. While GAP helps the convolution neural network to attend to the most discriminative…

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