Search Results for author:
Found 19 papers, 6 papers with code
Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras
We propose a head pose invariant gaze estimation model for distant RGB-D cameras.
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail.
Deep Neural Networks for Multiple Speaker Detection and Localization
We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction.
Theoretical Guarantees of Deep Embedding Losses Under Label Noise
Collecting labeled data to train deep neural networks is costly and even impractical for many tasks.
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image.
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples.
MuMMER: Socially Intelligent Human-Robot Interaction in Public Spaces
In the EU-funded MuMMER project, we have developed a social robot designed to interact naturally and flexibly with users in public spaces such as a shopping mall.
Real-time Convolutional Networks for Depth-based Human Pose Estimation
(i) we propose a fast and efficient network based on residual blocks (called RPM) for body landmark localization from depth images; (ii) we created a public dataset DIH comprising more than 170k synthetic images of human bodies with various shapes and viewpoints as well as real (annotated) data for evaluation; (iii) we show that our model trained on synthetic data from scratch can perform well on real data, obtaining similar results to larger models initialized with pre-trained networks.
Unsupervised Representation Learning for Gaze Estimation
Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups).
Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation
i) we study several CNN architecture designs combining pose machines relying on the cascade of detectors concept with lightweight and efficient CNN structures; ii) to address the need for large training datasets with high variability, we rely on semi-synthetic data combining multi-person synthetic depth data with real sensor backgrounds; iii) we explore domain adaptation techniques to address the performance gap introduced by testing on real depth images; iv) to increase the accuracy of our fast lightweight CNN models, we investigate knowledge distillation at several architecture levels which effectively enhance performance.
IEEE SLT 2021 Alpha-mini Speech Challenge: Open Datasets, Tracks, Rules and Baselines
In this challenge, we open source a sizable speech, keyword, echo and noise corpus for promoting data-driven methods, particularly deep-learning approaches on KWS and SSL.
Sound Audio and Speech Processing
Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation
We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios.
An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning
In this paper, we propose to frame the learning of pushing policies (where to push and how) by DQNs as an image-to-image translation problem and exploit an Hourglass-based architecture.
Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers
We propose to leverage Transformer architectures for non-autoregressive human motion prediction.
ChildPlay: A New Benchmark for Understanding Children's Gaze Behaviour
Furthermore, all publicly available gaze target prediction benchmarks mostly contain instances of adults, which makes models trained on them less applicable to scenarios with young children.
A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings
to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others.
Sharingan: A Transformer-based Architecture for Gaze Following
In this paper, we introduce a novel transformer-based architecture for 2D gaze prediction.
A Novel Framework for Multi-Person Temporal Gaze Following and Social Gaze Prediction
Gaze following and social gaze prediction are fundamental tasks providing insights into human communication behaviors, intent, and social interactions.
