The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
Multimodal Convolutional Neural Networks for Matching Image and Sentence
Abstract
In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.
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“An adaptive multimodal mapping between two visual modalities (e.g., images and sentences) typically requires strong representations of the individual modalities [213].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [213]
Citations
Cites
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Convolutional Neural Networks for Sentence Classification
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Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
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Flagged first, then by citations
2 of the 40 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- hinton, 2012, Improving neural networks by proventing co-adaptation of feature detecters, arXiv 1207 0580, 6
- kiros, 2014, Multimodal neural language model, ICML, 2
- sermanet, 2014, Overfeat: Intergrated recognition, localization and detection using convolutional networks, arXiv 1312 6229, 2
- girshick, 2014, Rich feature hierachies for accurate object detection and semantic segmentation, CVPR, 8
- ordonez, 2011, Im2txt: Describing images using 1 million captioned photogrphs, NIPS, 1
Cited by
Sorted by citations
A survey on deep learning for multimodal data fusion
Gao, J., Li, P., Chen, Z., & Zhang, J. (2020) · Neural Computation · doi:10.1162/neco_a_01273 · Cited by 821
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training
Li, G., Duan, N., Fang, Y., Gong, M., & Jiang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6795 · Cited by 795
Learning Deep Structure-Preserving Image-Text Embeddings
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Adversarial Cross-Modal Retrieval
Wang, B., Yang, Y., Xu, X., Hanjalic, A., & Shen, H. T. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123326 · Cited by 779
Dual Attention Networks for Multimodal Reasoning and Matching
Nam, H., Ha, J.-W., & Kim, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.232 · Cited by 760
Metadata
- Authors
- Ma, L., Lu, Z., Shang, L., & Li, H.
- Affiliations
- Huawei Technologies (China) (CN)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2623-2631
- OpenAlex
- W1916445035
- Cited by
- 334
- References
- 40
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 15 · 2023: 23 · 2022: 29 · 2021: 31 · 2020: 59 · 2019: 67 · 2018: 41 · 2017: 34 · 2016: 18 · 2015: 3
- FWCI
- 13.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- multimodal convolutional neural network, image retrieval, sentence retrieval, cross-modal matching, word composition, Flickr8k, Flickr30k
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