Person re-identification (re-ID) and attribute recognition share a common target at the pedestrian description. Their difference consists in the granularity. Attribute recognition focuses on local aspects of a person while person re-ID usually extracts global representations. Considering their similarity and difference, this paper proposes a very simple convolutional neural network (CNN) that learns a re-ID embedding and predicts the pedestrian attributes simultaneously. This multi-task method integrates an ID classification loss and a number of attribute classification losses, and back-propagates the weighted sum of the individual losses. Albeit simple, we demonstrate on two pedestrian benchmarks that by learning a more discriminative representation, our method significantly improves the re-ID baseline and is scalable on large galleries. We report competitive re-ID performance compared with the state-of-the-art methods on the two datasets.
Similar to the abovementioned paper, REF manually annotates two large-scale image re-ID datasets, and utilizes a multi-task CNN to learn a re-ID embedding with a combination of the ID classification loss and the attribute classification losses.
Improving Person Re-identification by Attribute and Identity Learning
ArXiv
ArXiv
Computer Science