  Person re-identification (re-ID) aims to recognize a person-of-interest
across different cameras with notable appearance variance. Existing research
works focused on the capability and robustness of visual representation. In
this paper, instead, we propose a novel hierarchical offshoot recurrent network
(HorNet) for improving person re-ID via image captioning. Image captions are
semantically richer and more consistent than visual attributes, which could
significantly alleviate the variance. We use the similarity preserving
generative adversarial network (SPGAN) and an image captioner to fulfill domain
transfer and language descriptions generation. Then the proposed HorNet can
learn the visual and language representation from both the images and captions
jointly, and thus enhance the performance of person re-ID. Extensive
experiments are conducted on several benchmark datasets with or without image
captions, i.e., CUHK03, Market-1501, and Duke-MTMC, demonstrating the
superiority of the proposed method. Our method can generate and extract
meaningful image captions while achieving state-of-the-art performance.
