activity
20172021
most citedUnsupervised Embedding Learning via Invariant and Spreading Instance Feature

79 citations · 154 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20212 cited

The Multi-Modal Video Reasoning and Analyzing Competition

Haoran Peng, He Huang, Li Xu +15

In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021. This competition is composed of four differen…

cs.CV2021

TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval

Yongbiao Chen, Sheng Zhang, Fangxin Liu +3

Deep hamming hashing has gained growing popularity in approximate nearest neighbour search for large-scale image retrieval. Until now, the deep hashing for the image retrieval comm…

cs.CV20208 cited

Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

Can Zhang, Hong Liu, Wei Guo +1

RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the…

cs.CV202032 cited

Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification

Mang Ye, Jianbing Shen, David J. Crandall +2

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Due to the large intra-class variations and cross-modality discrep…

cs.CV2020

Deep Learning for Person Re-identification: A Survey and Outlook

Mang Ye, Jianbing Shen, Gaojie Lin +3

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand…

cs.CV201979 cited

Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

Mang Ye, Xu Zhang, Pong C. Yuen +1

This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by…