  Person Search is a relevant task that aims to jointly solve Person Detection
and Person Re-identification(re-ID). Though most previous methods focus on
learning robust individual features for retrieval, it's still hard to
distinguish confusing persons because of illumination, large pose variance, and
occlusion. Contextual information is practically available in person search
task which benefits searching in terms of reducing confusion. To this end, we
present a novel contextual feature head named Attention Context-Aware
Embedding(ACAE) which enhances contextual information. ACAE repeatedly reviews
the person features within and across images to find similar pedestrian
patterns, allowing it to implicitly learn to find possible co-travelers and
efficiently model contextual relevant instances' relations. Moreover, we
propose Image Memory Bank to improve the training efficiency. Experimentally,
ACAE shows extensive promotion when built on different one-step methods. Our
overall methods achieve state-of-the-art results compared with previous
one-step methods.
