  Attention mechanism has been shown to be effective for person
re-identification (Re-ID). However, the learned attentive feature embeddings
which are often not naturally diverse nor uncorrelated, will compromise the
retrieval performance based on the Euclidean distance. We advocate that
enforcing diversity could greatly complement the power of attention. To this
end, we propose an Attentive but Diverse Network (ABD-Net), which seamlessly
integrates attention modules and diversity regularization throughout the entire
network, to learn features that are representative, robust, and more
discriminative. Specifically, we introduce a pair of complementary attention
modules, focusing on channel aggregation and position awareness, respectively.
Furthermore, a new efficient form of orthogonality constraint is derived to
enforce orthogonality on both hidden activations and weights. Through careful
ablation studies, we verify that the proposed attentive and diverse terms each
contributes to the performance gains of ABD-Net. On three popular benchmarks,
ABD-Net consistently outperforms existing state-of-the-art methods.
