  Scene-aware Complementary Item Retrieval (CIR) is a challenging task which
requires to generate a set of compatible items across domains. Due to the
subjectivity, it is difficult to set up a rigorous standard for both data
collection and learning objectives. To address this challenging task, we
propose a visual compatibility concept, composed of similarity (resembling in
color, geometry, texture, and etc.) and complementarity (different items like
table vs chair completing a group). Based on this notion, we propose a
compatibility learning framework, a category-aware Flexible Bidirectional
Transformer (FBT), for visual "scene-based set compatibility reasoning" with
the cross-domain visual similarity input and auto-regressive complementary item
generation. We introduce a "Flexible Bidirectional Transformer (FBT)"
consisting of an encoder with flexible masking, a category prediction arm, and
an auto-regressive visual embedding prediction arm. And the inputs for FBT are
cross-domain visual similarity invariant embeddings, making this framework
quite generalizable. Furthermore, our proposed FBT model learns the
inter-object compatibility from a large set of scene images in a
self-supervised way. Compared with the SOTA methods, this approach achieves up
to 5.3% and 9.6% in FITB score and 22.3% and 31.8% SFID improvement on fashion
and furniture, respectively.
