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Effective image retrieval with text feedback stands to impact a range ofreal-world applications, such as e-commerce. Given a source image and textfeedback that describes the desired modifications to that image, the goal is toretrieve the target images that resemble the source yet satisfy the givenmodifications by composing a multi-modal (image-text) query. We propose a novelsolution to this problem, Additive Attention Compositional Learning (AACL),that uses a multi-modal transformer-based architecture and effectively modelsthe image-text contexts. Specifically, we propose a novel image-textcomposition module based on additive attention that can be seamlessly pluggedinto deep neural networks. We also introduce a new challenging benchmarkderived from the Shopping100k dataset. AACL is evaluated on three large-scaledatasets (FashionIQ, Fashion200k, and Shopping100k), each with strongbaselines. Extensive experiments show that AACL achieves new state-of-the-artresults on all three datasets.

Image Search with Text Feedback by Additive Attention Compositional Learning