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Zhongdao Wang

20 papers hereh-index 143.1k citations25 works total

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

author position
  • first author6
  • middle author13

Across the 19 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CV19
  • cs.IR1
same name
  • Zhongdao Wang — 8 papers, h 7
  • Zhongdao Wang — 5 papers, h 2
  • Zhongdao Wang — 3 papers, h 2
  • Zhongdao Wang — 3 papers, h 3
  • Zhongdao Wang — 1 paper
  • Zhongdao Wang — 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

activity
20182024
most citedCircle Loss: A Unified Perspective of Pair Similarity Optimization

102 citations · 266 across the 20 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CV2019★ 16 cited

Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking

Yunzhong Hou, Liang Zheng, Zhongdao Wang +1

Multi-target multi-camera tracking (MTMCT) systems track targets across cameras. Due to the continuity of target trajectories, tracking systems usually restrict their data associat…

cs.CV2019★ 32 cited

Towards Real-Time Multi-Object Tracking

Zhongdao Wang, Liang Zheng, Yixuan Liu +2

Modern multiple object tracking (MOT) systems usually follow the \emph{tracking-by-detection} paradigm. It has 1) a detection model for target localization and 2) an appearance emb…

cs.CV2019★ 5 cited

Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning

Lanqing He, Zhongdao Wang, Yali Li +1

The softmax loss and its variants are widely used as objectives for embedding learning, especially in applications like face recognition. However, the intra- and inter-class object…

cs.CV2019★ 21 cited

Linkage Based Face Clustering via Graph Convolution Network

Zhongdao Wang, Liang Zheng, Yali Li +1

In this paper, we present an accurate and scalable approach to the face clustering task. We aim at grouping a set of faces by their potential identities. We formulate this task as…

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