# Composing Text and Image for Image Retrieval - An Empirical Odyssey

## TL;DR

This paper introduces a novel method for image retrieval that combines image and text inputs to find visually similar images with modifications, outperforming existing methods across multiple datasets.

## Contribution

We propose a new embedding and composition function specifically designed for image-text combined retrieval tasks, demonstrating superior performance over prior approaches.

## Key findings

- Outperforms existing methods on Fashion-200k, MIT-States, and CLEVR datasets
- Can be used for image classification of queries
- Effective in capturing modifications in images based on text descriptions

## Abstract

In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we learn a similarity metric between a target image and a source image plus source text, an embedding and composing function such that target image feature is close to the source image plus text composition feature. We propose a new way to combine image and text using such function that is designed for the retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to classify input queries, in addition to image retrieval.