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    <abstract>Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduce the model size and streamline their mobile-device deployment. Single- and dual-stream model structures are commonly used in image-text retrieval with the goal of closing the semantic gap between textual and visual modalities. While single-stream models use deep feature fusion to achieve more accurate cross-model alignment, dual-stream models are better at offline indexing and fast inference. We propose a Multi-teacher Cross-modality Alignment Distillation (MCAD) technique to integrate the advantages of single- and dual-stream models. By incorporating the fused single-stream features into the image and text features of the dual-stream model, we formulate new modified teacher similarity distributions and features. Then, we conduct both distribution and feature distillation to boost the capability of the student dual-stream model, achieving high retrieval performance without increasing inference complexity. Extensive experiments demonstrate the remarkable performance and high efficiency of MCAD on image-text retrieval tasks. Furthermore, we implement a lightweight CLIP model on Snapdragon/Dimensity chips with only ~100M running memory and ~8.0ms search latency, achieving the mobile-device application of VLP models.</abstract>
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%0 Conference Proceedings
%T MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
%A Lei, Youbo
%A He, Feifei
%A Chen, Chen
%A Mo, Yingbin
%A Li, Sijia
%A Xie, Defeng
%A Lu, Haonan
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lei-etal-2024-mcad
%X Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduce the model size and streamline their mobile-device deployment. Single- and dual-stream model structures are commonly used in image-text retrieval with the goal of closing the semantic gap between textual and visual modalities. While single-stream models use deep feature fusion to achieve more accurate cross-model alignment, dual-stream models are better at offline indexing and fast inference. We propose a Multi-teacher Cross-modality Alignment Distillation (MCAD) technique to integrate the advantages of single- and dual-stream models. By incorporating the fused single-stream features into the image and text features of the dual-stream model, we formulate new modified teacher similarity distributions and features. Then, we conduct both distribution and feature distillation to boost the capability of the student dual-stream model, achieving high retrieval performance without increasing inference complexity. Extensive experiments demonstrate the remarkable performance and high efficiency of MCAD on image-text retrieval tasks. Furthermore, we implement a lightweight CLIP model on Snapdragon/Dimensity chips with only ~100M running memory and ~8.0ms search latency, achieving the mobile-device application of VLP models.
%U https://aclanthology.org/2024.findings-naacl.96
%P 1491-1503

Markdown (Informal)

[MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval](https://aclanthology.org/2024.findings-naacl.96) (Lei et al., Findings 2024)

• MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval (Lei et al., Findings 2024)

ACL

• Youbo Lei, Feifei He, Chen Chen, Yingbin Mo, Sijia Li, Defeng Xie, and Haonan Lu. 2024. MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1491–1503, Mexico City, Mexico. Association for Computational Linguistics.