Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition
Abstract
Most existing feature learning-based methods for RGB-D object recognition either combine RGB and depth data in an undifferentiated manner from the outset, or learn features from color and depth separately, which do not adequately exploit different characteristics of the two modalities or utilize the shared relationship between the modalities. In this paper, we propose a general CNN-based multi-modal learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, which are then connected with a carefully designed multi-modal layer. This layer is designed to not only discover the most discriminative features for each modality, but is also able to harness the complementary relationship between the two modalities. The results of the multi-modal layer are back-propagated to update parameters of the CNN layers, and the multi-modal feature learning and the back-propagation are iteratively performed until convergence. Experimental results on two widely used RGB-D object datasets show that our method for general multi-modal learning achieves comparable performance to state-of-the-art methods specifically designed for RGB-D data.
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Fusing multi-modal data at the feature level is most prevalent, which can be further categorized into early fusion [186], late fusion [185], and fusion at multiple levels [85], [182].
“Among them, fusing multi-modal data at the feature level is most prevalent, which can be further categorized into three groups, i.e., early fusion [186], late fusion [185], and fusion at multiple levels [85], [182].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [185]
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Self-citation
- Authors
- Wang, A., Lu, J., Cai, J., Cham, T.-J., & Wang, G.
- Affiliations
- Nanyang Technological University (SG)
- Tsinghua University (CN)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2015-11
- Volume
- 17
- Issue
- 11
- Pages
- 1887-1898
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W1922904362
- Cited by
- 153
- References
- 8
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 9 · 2022: 14 · 2021: 12 · 2020: 19 · 2019: 23 · 2018: 29 · 2017: 26 · 2016: 8 · 2015: 2
- FWCI
- 18.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Funders
- Nanyang Technological University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- RGB-D object recognition, multimodal learning, large margin learning, representation learning
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