Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Abstract In Composed Image Retrieval (CIR), a user combines a query image with text to
describe their intended target. Existing methods rely on supervised learning of CIR models …

Abstract Composed Image Retrieval (CIR) aims to retrieve a target image based on a query
composed of a reference image and a relative caption that describes the difference between …

The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …

Conditioned and composed image retrieval extend CBIR systems by combining a query
image with an additional text that expresses the intent of the user, describing additional …

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 …

Composed image retrieval (CIR) task takes a composed query of image and text aiming to
search relative images for both conditions. Conventional CIR approaches need a training …

We study the zero-shot Composed Image Retrieval (ZS-CIR) task which is to retrieve the
target image given a reference image and a description without training on the triplet …

Fashion image retrieval based on a query pair of reference image and natural language
feedback is a challenging task that requires models to assess fashion related information …