Weakly Supervised Person Re-ID: Differentiable Graphical Learning and a New Benchmark.
@article{Wang2020WeaklySP, title={Weakly Supervised Person Re-ID: Differentiable Graphical Learning and a New Benchmark.}, author={Guangrun Wang and Guangcong Wang and Xujie Zhang and Jianhuang Lai and Zhengtao Yu and L. Lin}, journal={IEEE transactions on neural networks and learning systems}, year={2020}, volume={PP} }
Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 \cite{li2014deepreid} and Market-1501 \cite{zheng2015scalable}), which are quite expensive because each image in these datasets has to be assigned with a proper label. [...] Key Method The pseudo label is further used to supervise the learning of the Re-ID model. When compared with the fully supervised Re-ID models, our method achieves the state-of-the-art performance on SYSU-30$k$ and other…Expand Abstract
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SYSU-30k Dataset of "Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark" https://arxiv.org/abs/1904.03845
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