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researcher

Jiaxing Zhang

3 papers here

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

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.CL1
ORCID 0000-0002-1324-6486
same name
  • Jiaxing Zhang — 5 papers, h 12
  • Jiaxing Zhang — 1 paper, h 9
  • Jiaxing Zhang — 1 paper
  • Jiaxing Zhang — 1 paper

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

most citedScale-Invariant Convolutional Neural Networks

106 citations · 119 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2023★ 1 cited

Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning

Junyu Lu, Dixiang Zhang, Xiaojun Wu +5

Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs. However, suc…

cs.CV2023★ 1 cited

Improving Facade Parsing with Vision Transformers and Line Integration

Bowen Wang, Jiaxing Zhang, Ran Zhang +3

Facade parsing stands as a pivotal computer vision task with far-reaching applications in areas like architecture, urban planning, and energy efficiency. Despite the recent success…

cs.CV2014★ 12 cited

The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification

Tianjun Xiao, Yichong Xu, Kuiyuan Yang +3

Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the p…

cs.CV2014★ 106 cited

Scale-Invariant Convolutional Neural Networks

Yichong Xu, Tianjun Xiao, Jiaxing Zhang +2

Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited. The popul…

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