Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking

TL;DR

This paper introduces a training-free zero-shot composed image retrieval method that translates queries into explicit text and uses local concept re-ranking, achieving competitive results without costly triplet training.

Contribution

The work proposes a novel training-free approach with explicit query translation and local concept re-ranking, improving efficiency and performance in zero-shot image retrieval.

Findings

Achieves comparable performance to state-of-the-art triplet training methods.

Significantly outperforms other training-free methods on multiple benchmarks.

Effective in open domain and fashion-specific datasets.

Abstract

Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. It has recently attracted attention due to the collaboration of information-rich images and concise language to precisely express the requirements of target images. Most current composed image retrieval methods follow a supervised learning approach to training on a costly triplet dataset composed of a reference image, modified text, and a corresponding target image. To avoid difficult to-obtain labeled triplet training data, zero-shot composed image retrieval (ZS-CIR) has been introduced, which aims to retrieve the target image by learning from image-text pairs (self-supervised triplets), without the need for human-labeled triplets. However, this self-supervised triplet learning approach is computationally less effective and less understandable as it assumes the interaction between image and text is conducted with implicit query embedding without explicit semantical interpretation. In this work, we present a new training-free zero-shot composed image retrieval method which translates the query into explicit human-understandable text. This helps improve model learning efficiency to enhance the generalization capacity of foundation models. Further, we introduce a Local Concept Re-ranking (LCR) mechanism to focus on discriminative local information extracted from the modified instructions. Extensive experiments on four ZS-CIR benchmarks show that our method achieves comparable performances to that of the state of-the-art triplet training based methods, but significantly outperforms other training-free methods on the open domain datasets (CIRR, CIRCO and COCO), as well as the fashion domain dataset (FashionIQ).