A
Adrien Gaidon
Researcher at Toyota
Publications - 124
Citations - 6750
Adrien Gaidon is an academic researcher from Toyota. The author has contributed to research in topics: Deep learning & Object detection. The author has an hindex of 29, co-authored 119 publications receiving 4267 citations. Previous affiliations of Adrien Gaidon include Xerox & Microsoft.
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Virtual Worlds as Proxy for Multi-Object Tracking Analysis
TL;DR: This work proposes an efficient real-to-virtual world cloning method, and validate the approach by building and publicly releasing a new video dataset, called "Virtual KITTI", automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow.
Proceedings Article
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
TL;DR: A theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound is proposed that replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling.
Proceedings ArticleDOI
VirtualWorlds as Proxy for Multi-object Tracking Analysis
TL;DR: In this article, the authors proposed an efficient real-to-virtual world cloning method, and validated their approach by building and publicly releasing a new video dataset, called "Virtual KITTI" 1, automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow.
Proceedings ArticleDOI
3D Packing for Self-Supervised Monocular Depth Estimation
TL;DR: Li et al. as mentioned in this paper proposed a self-supervised monocular depth estimation method combining geometry with a new deep network, PackNet, learned only from unlabeled monocular videos, which leverages symmetrical packing and unpacking blocks to jointly learn to compress and decompress detail-preserving representations using 3D convolutions.
Proceedings ArticleDOI
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
TL;DR: This work proposes a novel loss formulation by lifting 2D detection, orientation, and scale estimation into 3D space and demonstrates that this approach doubles the AP on the 3D pose metrics on the official test set, defining the new state of the art.