  Weakly supervised person search aims to perform joint pedestrian detection
and re-identification (re-id) with only person bounding-box annotations.
Recently, the idea of contrastive learning is initially applied to weakly
supervised person search, where two common contrast strategies are memory-based
contrast and intra-image contrast. We argue that current intra-image contrast
is shallow, which suffers from spatial-level and occlusion-level variance. In
this paper, we present a novel deep intra-image contrastive learning using a
Siamese network. Two key modules are spatial-invariant contrast (SIC) and
occlusion-invariant contrast (OIC). SIC performs many-to-one contrasts between
two branches of Siamese network and dense prediction contrasts in one branch of
Siamese network. With these many-to-one and dense contrasts, SIC tends to learn
discriminative scale-invariant and location-invariant features to solve
spatial-level variance. OIC enhances feature consistency with the masking
strategy to learn occlusion-invariant features. Extensive experiments are
performed on two person search datasets CUHK-SYSU and PRW, respectively. Our
method achieves a state-of-the-art performance among weakly supervised one-step
person search approaches. We hope that our simple intra-image contrastive
learning can provide more paradigms on weakly supervised person search. The
source code is available at \url{https://github.com/jiabeiwangTJU/DICL}.
