Composed Image Retrieval (CIR) retrieves a target image similar to a reference image, guided by a provided textual modification (i.e., a triplet with). Previous works on CIR can largely be developed into two categories: supervised learning approaches and weakly supervised (i.e., zero-shot) learning approaches. Supervised learning CIR models require labeled triplets which may not be easily obtained and limit the widespread use of CIR and its scalability. On the other hand, a weakly supervised learning approach (also called zero-shot CIR), can be relatively easily trained with image-caption pairs without considering the image-to-image relation (i.e., no supervised triplet required), but this approach tends to yield lower accuracy. In this paper, we extend the application of existing Composed Image Retrieval (CIR) into semi-supervised learning, domain adaptation, and test-time adaptation contexts by exploiting only unlabeled image data. Previous approaches cannot be directly applied to these settings, as it is not trivial to leverage fully unlabeled data for CIR. To address this, we propose a new approach and settings where we identify a reference image and its associated target images in auxiliary image data. Our method involves training a large language model-based Visual Delta Generator (VDG) to produce textual descriptions of the visual differences (i.e., visual deltas) between these images. VDG, equipped with fluent language knowledge and being model agnostic, can generate pseudo-triplets to boost the performance of CIR models in diverse settings including semi-supervised CIR, domain adaptation, and test-time adaptation. Our approach significantly not only improves the existing supervised learning approaches and achieves state-of-the-art results on the CIR benchmarks but also expands the application of CIR across diverse settings.
