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Alexandre Massoud Alahi

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Publications associées (121)

Collaborative Sampling in Generative Adversarial Networks

Alexandre Massoud Alahi, Yuejiang Liu, Parth Ashit Kothari

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a co ...

2020

Next Steps for Image Synthesis using Semantic Segmentation

Alexandre Massoud Alahi, Saeed Saadatnejad

Image synthesis in the desired semantic can be used in many tasks of self-driving cars giving us the possibility to enhance existing challenging datasets by realistic-looking images which we do not have enough. Our goal is to improve the image quality gene ...

2020

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. These encompass visual ...

IEEE2020

Pedestrian Intention Prediction: A Multi-task Perspective

Alexandre Massoud Alahi, Saeed Saadatnejad

In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This w ...

2020

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

Alexandre Massoud Alahi, Radu Timofte

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representa ...

2020

Deep Visual Re-Identification with Confidence

Alexandre Massoud Alahi, George Adaimi, Sven Kreiss

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

2019

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss ...

2019

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

Alexandre Massoud Alahi

Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult IC ...

Springer2019

Feed-forwards meet recurrent networks in vehicle trajectory prediction

Alexandre Massoud Alahi, Mohammadhossein Bahari

For an autonomous car, getting surprised is the worst thing that can happen. To prevent that, plenty of studies are trying to forecast traffic participants’ actions especially in an urban scene using recurrent networks. Recurrent networks are used for temp ...

2019

PifPaf: Composite Fields for Human Pose Estimation

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts ...

IEEE2019

Convolutional Relational Machine for Group Activity Recognition

Alexandre Massoud Alahi

We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relationsbetween individualpersons in image or video. It learns to produce an ...

IEEE2019

Pedestrian Image Generation for Self-driving Cars

Alexandre Massoud Alahi, Saeed Saadatnejad

Pedestrian image generation in the desired pose can be used in a wide range of applications e.g., person re-identification and tracking which are among the fundamental challenges in self-driving cars. This is a hard task because it should be invariant to a ...

2019

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Alexandre Massoud Alahi, Yuejiang Liu, Sven Kreiss, Changan Chen

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

IEEE2019

Next Steps for Social Force with Big Data

Alexandre Massoud Alahi, Sven Kreiss

2019

Automatic Differentiation in Social Force Models

Alexandre Massoud Alahi, Sven Kreiss

2019

Learning online combinatorial stochastic policies with deep reinforcement

Alexandre Massoud Alahi, Teo Luke Stocco

2019

Adversarial Loss for Human Trajectory Prediction

Alexandre Massoud Alahi, Parth Ashit Kothari

2019

Using A Priori Knowledge to Improve Scene Understanding

Alexandre Massoud Alahi

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but ...

IEEE2019

Linear Artificial Forces for Human Dynamics in Complex Contexts

Alexandre Massoud Alahi

2018

Enhancing Discrete Choice Models with Neural Networks

Alexandre Massoud Alahi, Virginie Janine Camille Lurkin

2018

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