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Humans have an incredible ability to process and understand information from multiple sources such as images, video, text, and speech. Recent success of deep neural networks has enabled us to develop algorithms which give machines the ability to understand and interpret this information. There is a need to both broaden their applicability and develop methods which correlate visual information along with semantic content. We propose a unified model which jointly trains on images and captions, and learns to generate new captions given either an image or a caption query. We evaluate our model on three different tasks namely cross-modal retrieval, image captioning, and sentence paraphrasing. Our model gains insight into cross-modal vector embeddings, generalizes well on multiple tasks and is competitive to state of the art methods on retrieval.
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Peri and colleagues proposed a multimodal framework that encodes images and captions with CNN and RNN into an intermediate representation and decodes it into a new caption resembling the input.
“Similarly, Peri et al. [53] proposed a multimodal framework that encodes both images and captions using CNN and RNN as an intermediate level representation and then decodes these multimodal representations into a new caption that is similar to the input.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [53]
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Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
- Authors
- Peri, D., Sah, S., & Ptucha, R.
- Affiliations
- Rochester Institute of Technology (US)
- Venue
- 2019 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 295-299
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2970843291
- Cited by
- 4
- References
- 19
- Citations by year
- 2024: 1 · 2021: 2 · 2020: 1
- FWCI
- 0.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- cross-modal retrieval, image captioning, caption generation, cross-modal embeddings, unified multimodal models
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