415 citations · 616 across the 18 of their papers we have counts for
18 papers
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification
Mingkun Li, Chun-Guang Li, Jun Guo
Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID ar…
Knowledge Transfer Based Fine-grained Visual Classification
Siqing Zhang, Ruoyi Du, Dongliang Chang +2
Fine-grained visual classification (FGVC) aims to distinguish the sub-classes of the same category and its essential solution is to mine the subtle and discriminative regions. Conv…
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
Jiyang Xie, Zhanyu Ma, Jing-Hao Xue +2
This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based uncertainty inference (UI) in deep neural network (DNN)-based…
Fine-Grained Instance-Level Sketch-Based Video Retrieval
Peng Xu, Kun Liu, Tao Xiang +4
Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level ske…
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Dongliang Chang, Yifeng Ding, Jiyang Xie +6
Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex netw…
Scalable NAS with Factorizable Architectural Parameters
Lanfei Wang, Lingxi Xie, Tianyi Zhang +2
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision. The fundamental ideology of NAS is using an automatic mechanism to replace manual des…