159 citations · 561 across the 20 of their papers we have counts for
26 papers
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…
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…
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…
LODE: Deep Local Deblurring and A New Benchmark
Zerun Wang, Liuyu Xiang, Fan Yang +6
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from sev…