Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback

TL;DR

This paper introduces a new framework for multiturn conversational fashion image retrieval that effectively combines reference images, feedback, and attribute information, outperforming existing methods.

Contribution

The paper proposes a novel multiturn conversational fashion image retrieval framework that leverages mutual attention and a newly created large-scale dataset.

Findings

Significant performance improvement over state-of-the-art methods

Effective utilization of reference images and feedback in retrieval

Creation of a large-scale multiturn fashion dataset

Abstract

We study the task of conversational fashion image retrieval via multiturn natural language feedback. Most previous studies are based on single-turn settings. Existing models on multiturn conversational fashion image retrieval have limitations, such as employing traditional models, and leading to ineffective performance. We propose a novel framework that can effectively handle conversational fashion image retrieval with multiturn natural language feedback texts. One characteristic of the framework is that it searches for candidate images based on exploitation of the encoded reference image and feedback text information together with the conversation history. Furthermore, the image fashion attribute information is leveraged via a mutual attention strategy. Since there is no existing fashion dataset suitable for the multiturn setting of our task, we derive a large-scale multiturn fashion dataset via additional manual annotation efforts on an existing single-turn dataset. The experiments show that our proposed model significantly outperforms existing state-of-the-art methods.