Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context-Aware Visual Compatibility Prediction   How do we determine whether two or more clothing items are compatible or
visually appealing? Part of the answer lies in understanding of visual
aesthetics, and is biased by personal preferences shaped by social attitudes,
time, and place. In this work we propose a method that predicts compatibility
between two items based on their visual features, as well as their context. We
define context as the products that are known to be compatible with each of
these item. Our model is in contrast to other metric learning approaches that
rely on pairwise comparisons between item features alone. We address the
compatibility prediction problem using a graph neural network that learns to
generate product embeddings conditioned on their context. We present results
for two prediction tasks (fill in the blank and outfit compatibility) tested on
two fashion datasets Polyvore and Fashion-Gen, and on a subset of the Amazon
dataset; we achieve state of the art results when using context information and
show how test performance improves as more context is used.

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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph