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Found 3 papers, 1 papers with code
When Neural Networks Using Different Sensors Create Similar Features
Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... We draw from the well-developed analysis of similarity to provide an example of a problem where neural networks are trained from different sensors, and where the features extracted from these sensors still carry similar information.
The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection
Transport mode detection is a classification problem aiming to design an algorithm that can infer the transport mode of a user given multimodal signals (GPS and/or inertial sensors).
Data Fusion for Deep Learning on Transport Mode Detection: A Case Study
In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc.
