  Fashion products typically feature in compositions of a variety of styles at
different clothing parts. In order to distinguish images of different fashion
products, we need to extract both appearance (i.e., "how to describe") and
localization (i.e.,"where to look") information, and their interactions. To
this end, we propose a biologically inspired framework for image-based fashion
product retrieval, which mimics the hypothesized twostream visual processing
system of human brain. The proposed attentional heterogeneous bilinear network
(AHBN) consists of two branches: a deep CNN branch to extract fine-grained
appearance attributes and a fully convolutional branch to extract landmark
localization information. A joint channel-wise attention mechanism is further
applied to the extracted heterogeneous features to focus on important channels,
followed by a compact bilinear pooling layer to model the interaction of the
two streams. Our proposed framework achieves satisfactory performance on three
image-based fashion product retrieval benchmarks.
