Attend and Align: Improving Deep Representations with Feature Alignment Layer for Person Retrieval

In fine-grained recognition, object misalignment and background noise are two long-standing factors that influence the robustness of deep learning models. This paper mainly focuses on person re-identification (re-ID) and introduces a feature alignment layer (FAL) which alleviates the target misalignment and the background noise simultaneously. Through attention mechanism, FAL informs the underlying importance of each pixel on feature maps, i.e., whether the pixel is beneficial towards discriminating different persons. Then the discriminative regions relocate to the center and are stretched to fill the feature maps. Such an “attend and align” mechanism is specified into two steps: target position prediction and value assignment. In the first step, a pixel on feature maps learns to find a target position which is ID-discriminative. In the second step, the pixel is assigned with a new value using the context of the predicted position. Moreover, FAL can be easily plugged into a canonical Convolutional Neural Network (CNN) and learned in an end-to-end manner. In experiment, our method yields competitive results compared with the state-of-the-art approaches on three person re-ID datasets, Market-1501, DukeMTMC-reID and CUHK03. We also demonstrate that our method improves a competitive fine-grained recognition baseline on CUB-200-2011.