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Found 9 papers, 4 papers with code
Structure-Preserving Transformers for Sequences of SPD Matrices
In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries.
Ranked #1 on
Sleep Stage Detection
on MASS SS3
(Macro-averaged Accuracy metric)
Training face verification models from generated face identity data
We find that the addition of a small amount of private data greatly improves the performance of our model, which highlights the limitations of using synthetic data to train machine learning models.
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning
This model outperforms its teacher on novel tasks, achieving results on par with state-of-the-art methods on 15 facial analysis tasks (and domains), at an affordable training cost.
Multi-Level Sensor Fusion with Deep Learning
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors.
RPNet: an End-to-End Network for Relative Camera Pose Estimation
While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way.
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media.
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition.
CAKE: Compact and Accurate K-dimensional representation of Emotion
Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks. Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e. g. anger, happiness, sadness, etc.)
Ranked #24 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric)
TS-Net: Combining modality specific and common features for multimodal patch matching
Multimodal patch matching addresses the problem of finding the correspondences between image patches from two different modalities, e. g. RGB vs sketch or RGB vs near-infrared.
