  Cross-modal person re-identification (Re-ID) is critical for modern video
surveillance systems. The key challenge is to align cross-modality
representations induced by the semantic information present for a person and
ignore background information. This work presents a novel convolutional neural
network (CNN) based architecture designed to learn semantically aligned
cross-modal visual and textual representations. The underlying building block,
named AXM-Block, is a unified multi-layer network that dynamically exploits the
multi-scale knowledge from both modalities and re-calibrates each modality
according to shared semantics. To complement the convolutional design,
contextual attention is applied in the text branch to manipulate long-term
dependencies. Moreover, we propose a unique design to enhance visual part-based
feature coherence and locality information. Our framework is novel in its
ability to implicitly learn aligned semantics between modalities during the
feature learning stage. The unified feature learning effectively utilizes
textual data as a super-annotation signal for visual representation learning
and automatically rejects irrelevant information. The entire AXM-Net is trained
end-to-end on CUHK-PEDES data. We report results on two tasks, person search
and cross-modal Re-ID. The AXM-Net outperforms the current state-of-the-art
(SOTA) methods and achieves 64.44\% Rank@1 on the CUHK-PEDES test set. It also
outperforms its competitors by $>$10\% in cross-viewpoint text-to-image Re-ID
scenarios on CrossRe-ID and CUHK-SYSU datasets.
