Incremental deep hidden attribute learning

Person re-identifcation is a key technique to match person images captured in non-overlapping camera views. Due to the sensitivity of visual features to environmental changes, semantic attributes, such as “short-hair” or “long-hair”, begin to be investigated to represent person's appearance to improve the re-identifcation performance. Generally, training semantic attribute representations requires massive annotated samples, which limits the applicability on the large-scale practical applications. To alleviate the reliance on annotation efforts, we propose a new person representation with hidden attributes by mining latent information from visual feature in an unsupervised manner. In particular, an auto-encoder model is plugged-in to the deep learning network to compose a Deep Hidden Attribute Network (DHA-Net). The learnt hidden attribute representation preserves the robustness of semantic attributes and simultaneously inherits the discrimination ability of visual features. Experiments conducted on public datasets have validated the effectiveness of DHA-Net. On two large-scale datasets, i.e., Market-1501 and DukeMTMC-reID, the proposed method outperforms the state-of-the-art methods.