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Measuring similarity between two images often requires performing complexreasoning along different axes (e.g., color, texture, or shape). Insights intowhat might be important for measuring similarity can can be provided byannotated attributes, but prior work tends to view these annotations ascomplete, resulting in them using a simplistic approach of predictingattributes on single images, which are, in turn, used to measure similarity.However, it is impractical for a dataset to fully annotate every attribute thatmay be important. Thus, only representing images based on these incompleteannotations may miss out on key information. To address this issue, we proposethe Pairwise Attribute-informed similarity Network (PAN), which breakssimilarity learning into capturing similarity conditions and relevance scoresfrom a joint representation of two images. This enables our model to identifythat two images contain the same attribute, but can have it deemed irrelevant(e.g., due to fine-grained differences between them) and ignored for measuringsimilarity between the two images. Notably, while prior methods of usingattribute annotations are often unable to outperform prior art, PAN obtains a4-9% improvement on compatibility prediction between clothing items on PolyvoreOutfits, a 5% gain on few shot classification of images using Caltech-UCSDBirds (CUB), and over 1% boost to Recall@1 on In-Shop Clothes Retrieval.Implementation available at https://github.com/samarth4149/PAN

Effectively Leveraging Attributes for Visual Similarity