  We present an approach named the Cycled Composition Network that can measure
the semantic distance of the composition of image-text embedding. First, the
Composition Network transit a reference image to target image in an embedding
space using relative caption. Second, the Correction Network calculates a
difference between reference and retrieved target images in the embedding space
and match it with a relative caption. Our goal is to learn a Composition
mapping with the Composition Network. Since this one-way mapping is highly
under-constrained, we couple it with an inverse relation learning with the
Correction Network and introduce a cycled relation for given Image We
participate in Fashion IQ 2020 challenge and have won the first place with the
ensemble of our model.
