16 citations · 30 across the 5 of their papers we have counts for
7 papers
Online Convolutional Re-parameterization
Mu Hu, Junyi Feng, Jiashen Hua +4
Structural re-parameterization has drawn increasing attention in various computer vision tasks. It aims at improving the performance of deep models without introducing any inferenc…
Learning to Generate Content-Aware Dynamic Detectors
Junyi Feng, Jiashen Hua, Baisheng Lai +3
Model efficiency is crucial for object detection. Mostprevious works rely on either hand-crafted design or auto-search methods to obtain a static architecture, regardless ofthe dif…
Camera-aware Proxies for Unsupervised Person Re-Identification
Menglin Wang, Baisheng Lai, Jianqiang Huang +2
This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseu…
Towards Precise Intra-camera Supervised Person Re-identification
Menglin Wang, Baisheng Lai, Haokun Chen +3
Intra-camera supervision (ICS) for person re-identification (Re-ID) assumes that identity labels are independently annotated within each camera view and no inter-camera identity as…
Deep Active Learning for Video-based Person Re-identification
Menglin Wang, Baisheng Lai, Zhongming Jin +3
It is prohibitively expensive to annotate a large-scale video-based person re-identification (re-ID) dataset, which makes fully supervised methods inapplicable to real-world deploy…
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
Ken Chen, Fei Chen, Baisheng Lai +8
Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CN…