Outfit Completion via Conditional Set Transformation
Takuma Nakamura Yuki Saito Ryosuke Goto
In this paper, we formulate the outfit completion problem as a set retrieval
task and propose a novel framework for solving this problem. The proposal
includes a conditional set transformation architecture with deep neural
networks and a compatibility-based regularization method. The proposed method
utilizes a map with permutation-invariant for the input set and
permutation-equivariant for the condition set. This allows retrieving a set
that is compatible with the input set while reflecting the properties of the
condition set. In addition, since this structure outputs the element of the
output set in a single inference, it can achieve a scalable inference speed
with respect to the cardinality of the output set. Experimental results on real
data reveal that the proposed method outperforms existing approaches in terms
of accuracy of the outfit completion task, condition satisfaction, and
compatibility of completion results.