167 citations · 574 across the 21 of their papers we have counts for
23 papers
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Dengpan Fu, Dongdong Chen, Hao Yang +6
This paper aims to address the problem of pre-training for person re-identification (Re-ID) with noisy labels. To setup the pre-training task, we apply a simple online multi-object…
Online Multi-Object Tracking with Unsupervised Re-Identification Learning and Occlusion Estimation
Qiankun Liu, Dongdong Chen, Qi Chu +4
Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT), which often leads to inferior tracking results due to the missing detected objects. The c…
Unsupervised Finetuning
Suichan Li, Dongdong Chen, Yinpeng Chen +5
This paper studies "unsupervised finetuning", the symmetrical problem of the well-known "supervised finetuning". Given a pretrained model and small-scale unlabeled target data, uns…
MicroNet: Improving Image Recognition with Extremely Low FLOPs
Yunsheng Li, Yinpeng Chen, Xiyang Dai +6
This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e.g. 5M FLOPs on ImageNet classification). We found that two f…
Image Scene Graph Generation (SGG) Benchmark
Xiaotian Han, Jianwei Yang, Houdong Hu +3
There is a surge of interest in image scene graph generation (object, attribute and relationship detection) due to the need of building fine-grained image understanding models that…
Improve Unsupervised Pretraining for Few-label Transfer
Suichan Li, Dongdong Chen, Yinpeng Chen +5
Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance th…