EXTENDING HASHING TOWARDS FAST RE-IDENTIFICATION

Searching accuracy and efficiency are two challenges in person and vehicle Re-identification (Re-ID), which one focuses on robust representations learning which usually generating high-dimensional features while the other has not been fully explored. Hashing is a suitable solution to make Re-ID efficient. However, directly extending the existing hashing methods to fast Re-ID faces two challenges: one is the non-overlap between training and testing set which need more discriminative hash codes, the other is the large identities in Re-ID tasks which will lead to slow convergence and hard optimization. In this work, we propose an attention pooling operator to exploit both local and global visual attributes which can break limited discriminative power in hash methods. To further make training procedure converge faster and optimize the network more easily, we substitute non-differentiable L1-regularization with smooth L1-regularization. In experiments, our work outperforms state-of-the-art hashing and quantization methods on both person and vehicle Re-ID datasets. Besides, the results can serve as a strong baseline in the field of deep hashing for fast Re-ID.