iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2405.02951v1
• Date: Sun, 5 May 2024 14:39:06 GMT
• Title: iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval
• Authors: Lorenzo Agnolucci, Alberto Baldrati, Marco Bertini, Alberto Del Bimbo,
• Abstract summary: Composed Image Retrieval (CIR) aims to retrieve target images visually similar to the reference one while incorporating the changes specified in the relative caption. We introduce a new task, Zero-Shot CIR (ZS-CIR), that addresses CIR without the need for a labeled training dataset. We present an open-domain benchmarking dataset named CIRCO, where each query is labeled with multiple ground truths and a semantic categorization.
• Score: 26.101116761577796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a query consisting of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images visually similar to the reference one while incorporating the changes specified in the relative caption. The reliance of supervised methods on labor-intensive manually labeled datasets hinders their broad applicability. In this work, we introduce a new task, Zero-Shot CIR (ZS-CIR), that addresses CIR without the need for a labeled training dataset. We propose an approach named iSEARLE (improved zero-Shot composEd imAge Retrieval with textuaL invErsion) that involves mapping the visual information of the reference image into a pseudo-word token in CLIP token embedding space and combining it with the relative caption. To foster research on ZS-CIR, we present an open-domain benchmarking dataset named CIRCO (Composed Image Retrieval on Common Objects in context), the first CIR dataset where each query is labeled with multiple ground truths and a semantic categorization. The experimental results illustrate that iSEARLE obtains state-of-the-art performance on three different CIR datasets -- FashionIQ, CIRR, and the proposed CIRCO -- and two additional evaluation settings, namely domain conversion and object composition. The dataset, the code, and the model are publicly available at https://github.com/miccunifi/SEARLE.

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