107 citations · 243 across the 14 of their papers we have counts for
8 papers · 1 filter
Confidence Estimation via Auxiliary Models
Charles Corbière, Nicolas Thome, Antoine Saporta +3
Reliably quantifying the confidence of deep neural classifiers is a challenging yet fundamental requirement for deploying such models in safety-critical applications. In this paper…
Detecting 32 Pedestrian Attributes for Autonomous Vehicles
Taylor Mordan, Matthieu Cord, Patrick Pérez +1
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 p…
Toward unsupervised, multi-object discovery in large-scale image collections
Huy V. Vo, Patrick Pérez, Jean Ponce
This paper addresses the problem of discovering the objects present in a collection of images without any supervision. We build on the optimization approach of Vo et al. (CVPR'19)…
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
Adithya Ranga, Filippo Giruzzi, Jagdish Bhanushali +4
Advanced perception and path planning are at the core for any self-driving vehicle. Autonomous vehicles need to understand the scene and intentions of other road users for safe mot…
ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation
Antoine Saporta, Tuan-Hung Vu, Matthieu Cord +1
While fully-supervised deep learning yields good models for urban scene semantic segmentation, these models struggle to generalize to new environments with different lighting or we…
Handling new target classes in semantic segmentation with domain adaptation
Maxime Bucher, Tuan-Hung Vu, Matthieu Cord +1
In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w.r.t…
Learning Representations by Predicting Bags of Visual Words
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis +2
Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work…
Deep Reinforcement Learning for Autonomous Driving: A Survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert +4
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in…