73 citations · 173 across the 25 of their papers we have counts for
26 papers
When Neural Networks Using Different Sensors Create Similar Features
Hugues Moreau, Andréa Vassilev, Liming Chen
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 p…
The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection
Hugues Moreau, Andréa Vassilev, Liming Chen
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
Hugues Moreau, Andréa Vassilev, Liming Chen
In Transport Mode Detection, a great diversity of methodologies exist according to the choice made on sensors, preprocessing, model used, etc. In this domain, the comparisons betwe…
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Xiangnan Yin, Di Huang, Zehua Fu +2
Although much progress has been made recently in 3D face reconstruction, most previous work has been devoted to predicting accurate and fine-grained 3D shapes. In contrast, relativ…
Pixel Sampling for Style Preserving Face Pose Editing
Xiangnan Yin, Di Huang, Hongyu Yang +3
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image…
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization
Thomas Duboudin, Emmanuel Dellandréa, Corentin Abgrall +2
Traditional deep learning algorithms often fail to generalize when they are tested outside of the domain of the training data. The issue can be mitigated by using unlabeled data fr…