Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
This work addresses the limitation of composed image retrieval to narrow domains like fashion by enabling it on open-domain, real-life images, which is an incremental advancement for computer vision and language understanding.
The authors tackled the problem of composed image retrieval on complex, real-life images by introducing the CIRR dataset and proposing CIRPLANT, a transformer-based model that outperforms existing methods on open-domain images while matching state-of-the-art on narrow datasets.
We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reasoning in rich image and language contexts. To address this issue, we collect the Compose Image Retrieval on Real-life images (CIRR) dataset, which consists of over 36,000 pairs of crowd-sourced, open-domain images with human-generated modifying text. To extend current methods to the open-domain, we propose CIRPLANT, a transformer based model that leverages rich pre-trained vision-and-language (V&L) knowledge for modifying visual features conditioned on natural language. Retrieval is then done by nearest neighbor lookup on the modified features. We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion. Together with the release of CIRR, we believe this work will inspire further research on composed image retrieval.