Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition
By Anran Wang, Jiwen Lu, Jianfei Cai, Tat-Jen Cham, G. Wang. 2015
145 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Dacheng Tao
- Ling Shao
- Luc Van Gool
- Ming-Hsuan Yang
- Jianbing Shen
- Trevor Darrell
- Jitendra Malik
- Xiaogang Wang
Citations in the corpus
- Multimodal Multipart Learning for Action Recognition in Depth Videos (2015, background): “Fusion of RGB+D modalities is also studied for action recognition [13], [19] and for other problems [20], [21].”
- Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “, at the input level [181], at the feature level [85], [182], [183], [185], [186], and at the output level [184], respectively.”
- KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving (2018, background): “in the challenging scenarios to RGB images ( [13], [21]–[23]).”
- Cross-Modal Retrieval With CNN Visual Features: A New Baseline (2017, unknown): “[20], [22], [29], [33], [47], [49]–[52], [54], [57], [60], [62], [64], [66], [68] are also proposed to address multimodal problems.”
- Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis (2020, background): “[45] proposed a multi-modal deep learning framework for RGB-D object recognition, which simultaneously learns transformation matrices for two modalities and a maximal cross-modality correlation criterion.”
- Multi-Stream Deep Neural Networks for RGB-D Egocentric Action Recognition (2019, unknown): “, object recognition [65], person re-identification [66], etc.”
- Deep Multimodal Learning: A Survey on Recent Advances and Trends (2017, background): “Cost functions that enforce inter-and intramodality correlations have also been explored by Wang et al. [71].”
- Learning with Side Information through Modality Hallucination (2016, background): “Many of these methods do so by introducing new depth representations [19, 29, 32, 38, 36], most recently by adding an additional depth network representation into a convolutional network architecture [15, 14, 35].”