research-article

Cross-Modal Hybrid Feature Fusion for Image-Sentence Matching

Authors:

Heng Tao ShenAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 4

Article No.: 127, Pages 1 - 23

https://doi.org/10.1145/3458281

Published: 12 November 2021 Publication History

Abstract

Image-sentence matching is a challenging task in the field of language and vision, which aims at measuring the similarities between images and sentence descriptions. Most existing methods independently map the global features of images and sentences into a common space to calculate the image-sentence similarity. However, the image-sentence similarity obtained by these methods may be coarse as (1) an intermediate common space is introduced to implicitly match the heterogeneous features of images and sentences in a global level, and (2) only the inter-modality relations of images and sentences are captured while the intra-modality relations are ignored. To overcome the limitations, we propose a novel Cross-Modal Hybrid Feature Fusion (CMHF) framework for directly learning the image-sentence similarity by fusing multimodal features with inter- and intra-modality relations incorporated. It can robustly capture the high-level interactions between visual regions in images and words in sentences, where flexible attention mechanisms are utilized to generate effective attention flows within and across the modalities of images and sentences. A structured objective with ranking loss constraint is formed in CMHF to learn the image-sentence similarity based on the fused fine-grained features of different modalities bypassing the usage of intermediate common space. Extensive experiments and comprehensive analysis performed on two widely used datasets—Microsoft COCO and Flickr30K—show the effectiveness of the hybrid feature fusion framework in CMHF, in which the state-of-the-art matching performance is achieved by our proposed CMHF method.

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Index Terms

Cross-Modal Hybrid Feature Fusion for Image-Sentence Matching
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval
2. Information systems
  1. Information retrieval

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 4

November 2021

529 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3492437

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 12 November 2021

Accepted: 01 March 2021

Revised: 01 February 2021

Received: 01 November 2020

Published in TOMM Volume 17, Issue 4

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National Natural Science Foundation of China
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Sichuan Science and Technology Program, China

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Zeng RMa WWu XLiu WLiu J(2024)Image–Text Cross-Modal Retrieval with Instance Contrastive EmbeddingElectronics10.3390/electronics1302030013:2(300)Online publication date: 9-Jan-2024
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