Pseudo-triplet Guided Few-shot Composed Image Retrieval

• URL: http://arxiv.org/abs/2407.06001v1
• Date: Mon, 8 Jul 2024 14:53:07 GMT
• Title: Pseudo-triplet Guided Few-shot Composed Image Retrieval
• Authors: Bohan Hou, Haoqiang Lin, Haokun Wen, Meng Liu, Xuemeng Song,
• Abstract summary: Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image based on a multimodal query. We propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. Our scheme is plug-and-play and compatible with any existing supervised CIR models.
• Score: 20.130745490934597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image based on a multimodal query, i.e., a reference image and its corresponding modification text. While previous supervised or zero-shot learning paradigms all fail to strike a good trade-off between time-consuming annotation cost and retrieval performance, recent researchers introduced the task of few-shot CIR (FS-CIR) and proposed a textual inversion-based network based on pretrained CLIP model to realize it. Despite its promising performance, the approach suffers from two key limitations: insufficient multimodal query composition training and indiscriminative training triplet selection. To address these two limitations, in this work, we propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we employ a masked training strategy and advanced image caption generator to construct pseudo triplets from pure image data to enable the model to acquire primary knowledge related to multimodal query composition. In the second stage, based on active learning, we design a pseudo modification text-based query-target distance metric to evaluate the challenging score for each unlabeled sample. Meanwhile, we propose a robust top range-based random sampling strategy according to the 3-$\sigma$ rule in statistics, to sample the challenging samples for fine-tuning the pretrained model. Notably, our scheme is plug-and-play and compatible with any existing supervised CIR models. We tested our scheme across three backbones on three public datasets (i.e., FashionIQ, CIRR, and Birds-to-Words), achieving maximum improvements of 26.4%, 25.5% and 21.6% respectively, demonstrating our scheme's effectiveness.

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