Conditioned Image Retrieval for Fashion using Contrastive Learning and CLIP-based Features
Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto Del Bimbo
Abstract
Building on the recent advances in multimodal zero-shot representation learning, in this paper we explore the use of features obtained from the recent CLIP model to perform conditioned image retrieval. Starting from a reference image and an additive textual description of what the user wants with respect to the reference image, we learn a Combiner network that is able to understand the image content, integrate the textual description and provide combined feature used to perform the conditioned image retrieval. Starting from the bare CLIP features and a simple baseline, we show that a carefully crafted Combiner network, based on such multimodal features, is extremely effective and outperforms more complex state of the art approaches on the popular FashionIQ dataset.