  Attribute-based person search is in significant demand for applications where
no detected query images are available, such as identifying a criminal from
witness. However, the task itself is quite challenging because there is a huge
modality gap between images and physical descriptions of attributes. Often,
there may also be a large number of unseen categories (attribute combinations).
The current state-of-the-art methods either focus on learning better
cross-modal embeddings by mining only seen data, or they explicitly use
generative adversarial networks (GANs) to synthesize unseen features. The
former tends to produce poor embeddings due to insufficient data, while the
latter does not preserve intra-class compactness during generation. In this
paper, we present a symbiotic adversarial learning framework, called SAL.Two
GANs sit at the base of the framework in a symbiotic learning scheme: one
synthesizes features of unseen classes/categories, while the other optimizes
the embedding and performs the cross-modal alignment on the common embedding
space .Specifically, two different types of generative adversarial networks
learn collaboratively throughout the training process and the interactions
between the two mutually benefit each other. Extensive evaluations show SAL's
superiority over nine state-of-the-art methods with two challenging pedestrian
benchmarks, PETA and Market-1501. The code is publicly available at:
https://github.com/ycao5602/SAL .
