  Many previous methods on text-based person retrieval tasks are devoted to
learning a latent common space mapping, with the purpose of extracting
modality-invariant features from both visual and textual modality.
Nevertheless, due to the complexity of high-dimensional data, the unconstrained
mapping paradigms are not able to properly catch discriminative clues about the
corresponding person while drop the misaligned information. Intuitively, the
information contained in visual data can be divided into person information
(PI) and surroundings information (SI), which are mutually exclusive from each
other. To this end, we propose a novel Deep Surroundings-person Separation
Learning (DSSL) model in this paper to effectively extract and match person
information, and hence achieve a superior retrieval accuracy. A
surroundings-person separation and fusion mechanism plays the key role to
realize an accurate and effective surroundings-person separation under a
mutually exclusion constraint. In order to adequately utilize multi-modal and
multi-granular information for a higher retrieval accuracy, five diverse
alignment paradigms are adopted. Extensive experiments are carried out to
evaluate the proposed DSSL on CUHK-PEDES, which is currently the only
accessible dataset for text-base person retrieval task. DSSL achieves the
state-of-the-art performance on CUHK-PEDES. To properly evaluate our proposed
DSSL in the real scenarios, a Real Scenarios Text-based Person Reidentification
(RSTPReid) dataset is constructed to benefit future research on text-based
person retrieval, which will be publicly available.
