159 citations · 943 across the 39 of their papers we have counts for
7 papers · 1 filter
Confidence-guided Centroids for Unsupervised Person Re-Identification
Yunqi Miao, Jiankang Deng, Guiguang Ding +1
Unsupervised person re-identification (ReID) aims to train a feature extractor for identity retrieval without exploiting identity labels. Due to the blind trust in imperfect cluste…
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
Fan Yang, Xinhao Xu, Hui Chen +4
The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD). However, it has been neglected by most mainstream methods. In this paper, we ide…
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System
Xin Zhao, Zhiwei Fang, Yuchen Guo +3
A combinatorial recommender (CR) system feeds a list of items to a user at a time in the result page, in which the user behavior is affected by both contextual information and item…
A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets
Hehan Teng, Tao He, Yuchen Guo +1
Supervised person re-identification methods rely heavily on high-quality cross-camera training label. This significantly hinders the deployment of re-ID models in real-world applic…
Re-parameterizing Your Optimizers rather than Architectures
Xiaohan Ding, Honghao Chen, Xiangyu Zhang +3
The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to train…
A Free Lunch to Person Re-identification: Learning from Automatically Generated Noisy Tracklets
Hehan Teng, Tao He, Yuchen Guo +2
A series of unsupervised video-based re-identification (re-ID) methods have been proposed to solve the problem of high labor cost required to annotate re-ID datasets. But their per…
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…