Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained Alignment

• URL: http://arxiv.org/abs/2308.14009v1
• Date: Sun, 27 Aug 2023 05:45:54 GMT
• Title: Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained Alignment
• Authors: Jiamin Zhuang, Jing Yu, Yang Ding, Xiangyan Qu, Yue Hu
• Abstract summary: We propose an image-text alignment module SelfAlign on top of the independent-embedding framework. SelfAlign forces image-text alignment at both concept level and context level by self-supervised contrastive learning. It consistently boosts the accuracy of state-of-the-art non-pretraining independent-embedding models respectively by 9.1%, 4.2% and 6.6% in terms of R@sum score on Flickr30K, MSCOCO 1K and MS-COCO 5K datasets.
• Score: 9.183014914635553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-text retrieval requires the system to bridge the heterogenous gap between vision and language for accurate retrieval while keeping the network lightweight-enough for efficient retrieval. Existing trade-off solutions mainly study from the view of incorporating cross-modal interactions with the independent-embedding framework or leveraging stronger pretrained encoders, which still demand time-consuming similarity measurement or heavyweight model structure in the retrieval stage. In this work, we propose an image-text alignment module SelfAlign on top of the independent-embedding framework, which improves the retrieval accuracy while maintains the retrieval efficiency without extra supervision. SelfAlign contains two collaborative sub-modules that force image-text alignment at both concept level and context level by self-supervised contrastive learning. It does not require cross-modal embedding interactions during training while maintaining independent image and text encoders during retrieval. With comparable time cost, SelfAlign consistently boosts the accuracy of state-of-the-art non-pretraining independent-embedding models respectively by 9.1%, 4.2% and 6.6% in terms of R@sum score on Flickr30K, MSCOCO 1K and MS-COCO 5K datasets. The retrieval accuracy also outperforms most existing interactive-embedding models with orders of magnitude decrease in retrieval time. The source code is available at: https://github.com/Zjamie813/SelfAlign.

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