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For fashion outfits to be considered aesthetically pleasing, the garmentsthat constitute them need to be compatible in terms of visual aspects, such asstyle, category and color. Previous works have defined visual compatibility asa binary classification task with items in a garment being considered as fullycompatible or fully incompatible. However, this is not applicable to OutfitMaker applications where users create their own outfits and need to know whichspecific items may be incompatible with the rest of the outfit. To addressthis, we propose the Visual InCompatibility TransfORmer (VICTOR) that isoptimized for two tasks: 1) overall compatibility as regression and 2) thedetection of mismatching items and utilize fashion-specific contrastivelanguage-image pre-training for fine tuning computer vision neural networks onfashion imagery. We build upon the Polyvore outfit benchmark to generatepartially mismatching outfits, creating a new dataset termed Polyvore-MISFITs,that is used to train VICTOR. A series of ablation and comparative analysesshow that the proposed architecture can compete and even surpass the currentstate-of-the-art on Polyvore datasets while reducing the instance-wise floatingoperations by 88%, striking a balance between high performance and efficiency.We release our code athttps://github.com/stevejpapad/Visual-InCompatibility-Transformer

VICTOR: Visual Incompatibility Detection with Transformers and Fashion-specific contrastive pre-training