Composed Image Retrieval (CIR) involves searching for target images based on an image-text pair query. While current methods treat this as a query-target matching problem, we argue that CIR triplets contain additional associations beyond this primary relation. In our paper, we identify two new relations within triplets, treating each triplet as a graph node. Firstly, we introduce the concept of text-bridged image alignment, where the query text serves as a bridge between the query image and the target image. We propose a hinge-based cross-attention mechanism to incorporate this relation into network learning. Secondly, we explore complementary text reasoning, considering CIR as a form of cross-modal retrieval where two images compose to reason about complementary text. To integrate these perspectives effectively, we design a twin attention-based compositor. By combining these complementary associations with the explicit query pair-target image relation, we establish a comprehensive set of constraints for CIR. Our framework, CaLa (Complementary Association Learning for Augmenting Composed Image Retrieval), leverages these insights. We evaluate CaLa on CIRR and FashionIQ benchmarks with multiple backbones, demonstrating its superiority in composed image retrieval.

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When scenes created by the AI image generator DALL-E started circulating online earlier this year, it seemed inevitable that someone would turn the technology to fashion. DALL-E is part of a new crop of AI capable of creating extraordinarily detailed and realistic imagery from a text prompt, making it easy for anyone to use. Artists have quickly begun applying these programs to creating digital art, with one piece conjured up by the program Midjourney even beating out its human-generated competition for a prize. The same power could easily be used to whip up clothing designs. The idea is already becoming reality.

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. The DALL-E API won't be officially announced until later this fall, according to OpenAI, but today the company shared details about a customer already leveraging the DALL-E API for a specific enterprise use case. New York City-based Cala, a startup that bills itself as the "world's first operating system for fashion," offers a digital platform (including a mobile app launched in March) that allows creators to design and produce clothing lines, unifying the process from product ideation through order fulfillment. With the addition of DALL-E-powered text-to-image generating tools, users can generate new visual design ideas from natural text descriptions or uploaded reference images – which the company says are first-of-its-kind capabilities for the fashion industry.