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Jian Xu

4 papers hereh-index 5131 citations7 works total

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

author position
  • first author4

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

fields
  • cs.CV4
same name
  • Jian Xu — 34 papers, h 5
  • Jian Xu — 31 papers, h 21
  • Jian Xu — 18 papers, h 5
  • Jian Xu — 16 papers, h 22
  • Jian Xu — 15 papers, h 11
  • Jian Xu — 13 papers, h 5

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 citedUnsupervised Semantic-based Aggregation of Deep Convolutional Features

45 citations · 76 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2018★ 1 cited

Adversarial Soft-detection-based Aggregation Network for Image Retrieval

Jian Xu, Chunheng Wang, Cunzhao Shi +1

In recent year, the compact representations based on activations of Convolutional Neural Network (CNN) achieve remarkable performance in image retrieval. However, retrieval of some…

cs.CV2018★ 45 cited

Unsupervised Semantic-based Aggregation of Deep Convolutional Features

Jian Xu, Chunheng Wang, Chengzuo Qi +2

In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as seman…

cs.CV2017★ 4 cited

Iterative Manifold Embedding Layer Learned by Incomplete Data for Large-scale Image Retrieval

Jian Xu, Chunheng Wang, Chengzuo Qi +2

Existing manifold learning methods are not appropriate for image retrieval task, because most of them are unable to process query image and they have much additional computational…

cs.CV2017★ 26 cited

Unsupervised Part-based Weighting Aggregation of Deep Convolutional Features for Image Retrieval

Jian Xu, Cunzhao Shi, Chengzuo Qi +2

In this paper, we propose a simple but effective semantic part-based weighting aggregation (PWA) for image retrieval. The proposed PWA utilizes the discriminative filters of deep c…

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