Noise-Aware Person Re-identification via Local Uncertainty Estimation

Chunli Song, Yucan Zhou, Wenqiu Zhu, Jiang Zhou, Xiaoyan Gu, Bo Li

Abstract: Person re-identification aims to retrieve the target person from a large-scale dataset, and its performance highly relies on a large, well-annotated training dataset. However, label noise generated by manual mislabeling is usually inevitable in real-world scenarios. An intuitive way to alleviate the effect of label noise is to identify and discard noisy data. Recently, most works recognize noisy samples with their global features, which will omit some noisy samples when they are subtly different from the clean ones. In this paper, we propose to recognize noisy data with local features based on uncertainty estimation. Specifically, for each input image, we randomly erase a part of its features to generate a local feature and propagate it to the softmax layer to obtain a local soft label. Then, we repeat the above operations to obtain multiple local soft labels. Subsequently, we estimate the uncertainty with these multiple local soft labels and utilize them to identify whether this sample is noisy. Finally, we design a noise-aware cross-entropy loss based on the estimated uncertainty to reduce the effect of noisy samples automatically. Experimental results on Market-1501 and CUHK03 datasets show the effectiveness of our proposed model, where at least 4.8% and 1.5% improvements are achieved compared with other methods under 0% and 10% noise settings on Market-1501 datasets. Code is available at https://github.com/songchunli1999/NACE.