Publication
Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures
Publications associées (44)
Towards learning-based denoising of light fields
Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith
In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...
2023Estimating and Improving the Robustness of Attributions in Text
End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...
EPFL2023Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting
Alexandre Massoud Alahi, Parth Ashit Kothari
Human trajectory forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. Following the success of Social Generative Adversarial Networks (SGAN), recent works propose various GAN- ...
2022LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals
Pascal Fua, Pavan P Ramdya, Adám Gosztolai, Victor Lobato Rios, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, Marco Pietro Abrate
LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques. Markerless three-dimensional (3D) pose estimation has become ...
NATURE PORTFOLIO2021Tracking and Relative Localization of Drone Swarms With a Vision-Based Headset
Dario Floreano, Fabrizio Schiano, Maxim Pavliv, Giuseppe Loianno
We address the detection, tracking, and relative localization of the agents of a drone swarm from a human perspective using a headset equipped with a single camera and an Inertial Measurement Unit (IMU). We train and deploy a deep neural network detector o ...
2021From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search
Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...
EPFL2021Learning stereo reconstruction with deep neural networks
Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed.
The main drawback of these ...
EPFL2020A Simple, Inexpensive, Wearable Glove with Hybrid Resistive-Pressure Sensors for Computational Sensing, Proprioception, and Task Identification
Wearable devices have many applications ranging from health analytics to virtual and mixed reality interaction, to industrial training. For wearable devices to be practical, they must be responsive, deformable to fit the wearer, and robust to the user's ra ...
John Wiley and Sons Inc.2020XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
Pascal Fua, Helge Jochen Rhodin, Gerard Pons Moll
We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates successfully in generic scenes which may contain occlusions by objects and by other people. Our method operates in subsequent stages. T ...
ASSOC COMPUTING MACHINERY2020Neural Anisotropy Directions
Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas
In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depen ...
2020