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Liang Zheng

84 papers hereh-index 6436.8k citations125 works total

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

author position
  • first author9
  • middle author48
  • last author24

Across the 81 of 84 papers where every author was matched, so the position is known.

fields
  • cs.CV79
  • cs.LG2
  • cs.IR1
  • cs.MM1
  • math.AP1
same name
  • Liang Zheng — 18 papers, h 13
  • Liang Zheng — 17 papers, h 4
  • Liang Zheng — 13 papers, h 6
  • Liang Zheng — 12 papers, h 8
  • Liang Zheng — 10 papers, h 4
  • Liang Zheng — 7 papers, h 2

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
20142023
most citedPerson Re-identification: Past, Present and Future

1k citations · 7.4k across the 81 of their papers we have counts for

collaborators
Showing 2014Show all

4 papers · 1 filter

cs.CV2014

Visual Reranking with Improved Image Graph

Ziqiong Liu, Shengjin Wang, Liang Zheng +1

This paper introduces an improved reranking method for the Bag-of-Words (BoW) based image search. Built on [1], a directed image graph robust to outlier distraction is proposed. In…

cs.CV2014★ 16 cited

Seeing the Big Picture: Deep Embedding with Contextual Evidences

Liang Zheng, Shengjin Wang, Fei He +1

In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance. Conventionally, local cues of…

cs.CV2014

Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

Liang Zheng, Shengjin Wang, Wengang Zhou +1

The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artif…

cs.CV2014★ 32 cited

Packing and Padding: Coupled Multi-index for Accurate Image Retrieval

Liang Zheng, Shengjin Wang, Ziqiong Liu +1

In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss durin…

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