Self-Augmented Multi-Modal Feature Embedding
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Abstract (arXiv)
Oftentimes, patterns can be represented through different modalities. For example, leaf data can be in the form of images or contours. Handwritten characters can also be either online or offline. To exploit this fact, we propose the use of self-augmentation and combine it with multi-modal feature embedding. In order to take advantage of the complementary information from the different modalities, the self-augmented multi-modal feature embedding employs a shared feature space. Through experimental results on classification with online handwriting and leaf images, we demonstrate that the proposed method can create effective embeddings.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 8 Mar 2021.
Builds on
13 of 22 references.
References · 13
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- 2017CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learningcited by 27
- 2019Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognitioncited by 12
- 2017Multimodal Classification for Analysing Social Mediacited by 8
- 2019Modality Conversion of Handwritten Patterns by Cross Variational Autoencoderscited by 8
- 2016Image-Text Multi-Modal Representation Learning by Adversarial Backpropagationcited by 2
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1 paper.