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Zhiyong Liu

4 papers hereh-index 181.6k citations88 works total

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

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Zhiyong Liu — 8 papers, h 7
  • Zhiyong Liu — 4 papers, h 28
  • Zhiyong Liu — 4 papers, h 6
  • Zhiyong Liu — 4 papers, h 2
  • Zhiyong Liu — 3 papers, h 3
  • Zhiyong Liu — 2 papers, h 20

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 citedWeakly Aligned Feature Fusion for Multimodal Object Detection

97 citations · 98 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 1 cited

Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

Lu Zhang, Lu Qi, Xu Yang +3

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to…

cs.CV2022★ 97 cited

Weakly Aligned Feature Fusion for Multimodal Object Detection

Lu Zhang, Zhiyong Liu, Xiangyu Zhu +4

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data of…

cs.CV2022

Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation

Lu Zhang, Siqi Zhang, Xu Yang +2

Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features…

cs.CV2019

Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection

Lu Zhang, Xiangyu Zhu, Xiangyu Chen +3

Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image.…

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