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Improving Image Retrieval Precision with a Dual-Branch Network and Multi-Scale Contextual Collaboration

Published: 23 May 2024 Publication History

Abstract

This paper introduces a dual-branch fine-grained image retrieval method based on cross-scale feature fusion. Initially, visual features are extracted from input images using a convolutional neural network. Subsequently, a channel weighting module is employed to finely weight channel features, enhancing the coordination between different feature maps. Following that, we incorporate a spatial pyramid pooling module to efficiently fuse global contextual information in the global branch. In the local branch, we employ ASPP modules and self-attention mechanisms to better extract representative local information. Finally, we introduce our proposed multi-scale contextual collaboration module, which seamlessly integrates global features with local features, further reducing the semantic gap between different feature scales. Extensive experiments were conducted using multiple datasets, including fiar-10, cifar-100, and ImageNet, demonstrating the outstanding performance of our method in image retrieval tasks.

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      ICAICE '23: Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering
      November 2023
      1263 pages
      ISBN:9798400708831
      DOI:10.1145/3652628
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 23 May 2024

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