MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval

• URL: http://arxiv.org/abs/2310.19654v3
• Date: Tue, 2 Apr 2024 00:12:21 GMT
• Title: MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
• Authors: Youbo Lei, Feifei He, Chen Chen, Yingbin Mo, Si Jia Li, Defeng Xie, Haonan Lu,
• Abstract summary: We propose a Multi-teacher Cross-modality Alignment Distillation (MCAD) technique to integrate the advantages of single- and dual-stream models. We implement a lightweight CLIP model on Snapdragon/Dimensity chips with only $sim$100M running memory and $sim$8.0ms search latency.
• Score: 7.233106731197739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $\sim$100M running memory and $\sim$8.0ms search latency, achieving the mobile-device application of VLP models.

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