Abstract
Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. It has recently attracted attention due to the collaboration of information-rich images and concise language to precisely express the requirements of target images. Most current composed image retrieval methods follow a supervised learning approach to training on a costly triplet dataset composed of a reference image, modified text, and a corresponding target image. To avoid difficult to-obtain labeled triplet training data, zero-shot composed image retrieval (ZS-CIR) has been introduced, which aims to retrieve the target image by learning from image-text pairs (self-supervised triplets), without the need for human-labeled triplets. However, this self-supervised triplet learning approach is computationally less effective and less understandable as it assumes the interaction between image and text is conducted with impl