Radu, V., et al.: Multimodal deep learning for activity and context recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 157:1–157:27 (2018) Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. ...
The goal of this article is to provide a comprehensive survey on deep multimodal representation learning and suggest the future direction in this active field.Generally,themachine learning tasks based on multimodal data include three necessary steps: modality-specific features extracting, multimodal represe...
He, “Learning from the master: Distilling cross-modal advanced knowledge for lip reading,” in CVPR, 2021. [78] P. Xu, T. M. Hospedales, Q. Yin, Y.-Z. Song, T. Xiang, and L. Wang, “Deep learning for free-hand sketch: A survey,” TPAMI, 2022. [79] Y. Li, S. Rao...
Despite its impressive empirical performance, the theoretical foundations of deep multi-modal learning have yet to be fully explored. In this paper, we will undertake a comprehensive survey of recent devel‐ opments in multi-modal learning theories, focusing on the fundamental propert...
Section 3 reviews the existing deep multimodal segmentation methods according to our taxonomy of fusion strategy, followed by a brief discussion on architectural design. Section 4 provides a broad survey of current unimodal and multimodal image segmentation datasets. Several typical modalities (e.g., ...
Deep multi-modal learning architectures capable of handling four and five modalities have also been reported [34], [35], [36]. Their goal is to effectively capture patterns from temporal data (video, co-motion, audio) and explore spatio-temporal relationships. Existing reviews of deep learning-...
Deep learning-based approaches using CNNs and LSTMs embed the input modalities before training event detectors on these learned embeddings. Because of their expensive training time, these models are often partially pre-trained, especially for text analysis, where model parameters are fitted to large ...
A survey on deep transfer learning. In International conference on artificial neural networks 270–279 (Springer, Cham, 2018). Breiman, L. Stacked regressions. Mach. Learn. 24, 49–64 (1996). Article Google Scholar Wolpert, D. Stacked generalization. Neural Netw. 5, 241–259 (1992). ...
1, which is queired on July 17, 2019. We can observe that the number of papers increases every year from 2014 to 2018, which means multi-modal medical image segmentation in deep learning are obtaining more and more attention in recent years. To have a better understanding of the dimension...
Unlike traditional deep learning models that improve classification accuracy by increasing depth, the proposed model in this paper not only improves classification accuracy, but also achieves the light weight of the model. The work in this paper is organized as follows: “Multi-modal PQD classificatio...