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Feng Lu

4 papers hereh-index 464 citations8 works total

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

author position
  • first author2
  • middle author1
  • last author1

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

fields
  • cs.CV4
same name
  • Feng Lu — 32 papers, h 33
  • Feng Lu — 10 papers
  • Feng Lu — 9 papers, h 10
  • Feng Lu — 4 papers, h 1
  • Feng Lu — 4 papers, h 5
  • Feng Lu — 4 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

most citedSTA-VPR: Spatio-temporal Alignment for Visual Place Recognition

28 citations · 28 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2023

AANet: Aggregation and Alignment Network with Semi-hard Positive Sample Mining for Hierarchical Place Recognition

Feng Lu, Lijun Zhang, Shuting Dong +2

Visual place recognition (VPR) is one of the research hotspots in robotics, which uses visual information to locate robots. Recently, the hierarchical two-stage VPR methods have be…

cs.CV2023

SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations

Zhiming Wang, Lin Gu, Feng Lu

Due to the prevalence of scale variance in nature images, we propose to use image scale as a self-supervised signal for Masked Image Modeling (MIM). Our method involves selecting r…

cs.CV2023

NPR: Nocturnal Place Recognition in Streets

Bingxi Liu, Yujie Fu, Feng Lu +3

Visual Place Recognition (VPR) is the task of retrieving database images similar to a query photo by comparing it to a large database of known images. In real-world applications, e…

cs.CV2021★ 28 cited

STA-VPR: Spatio-temporal Alignment for Visual Place Recognition

Feng Lu, Baifan Chen, Xiang-Dong Zhou +1

Recently, the methods based on Convolutional Neural Networks (CNNs) have gained popularity in the field of visual place recognition (VPR). In particular, the features from the midd…

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