Patrick Pérez

PIE: Portrait Image Embedding for Semantic Control

Sep 20, 2020
Ayush Tewari, Mohamed Elgharib, Mallikarjun B R., Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt

* To appear in SIGGRAPH Asia 2020. Project webpage: https://gvv.mpi-inf.mpg.de/projects/PIE/

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VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users

Jul 10, 2020
Adithya Ranga, Filippo Giruzzi, Jagdish Bhanushali, Emilie Wirbel, Patrick Pérez, Tuan-Hung Vu, Xavier Perrotton

* This paper is reprinted from, "VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users, IS&T Electronic Imaging: Autonomous Vehicles and Machines 2020 Proceedings, (IS&T, Springfield, VA, 2020) page 109-1-10. DOI: 10.2352/ISSN.2470-1173.2020.16.AVM-109." Reprinted with permission of The Society for Imaging Science and Technology, holders of the 2020 copyright

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Patrick Pérez

PIE: Portrait Image Embedding for Semantic Control

VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users

Toward unsupervised, multi-object discovery in large-scale image collections

Spherical Perspective on Learning with Batch Norm

ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation

Photo style transfer with consistency losses

BUDA: Boundless Unsupervised Domain Adaptation in Semantic Segmentation

StyleRig: Rigging StyleGAN for 3D Control over Portrait Images

Learning Representations by Predicting Bags of Visual Words

Deep Reinforcement Learning for Autonomous Driving: A Survey

Scattering Features for Multimodal Gait Recognition

QUEST: Quantized embedding space for transferring knowledge

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

This dataset does not exist: training models from generated images

Addressing Failure Prediction by Learning Model Confidence

Boosting Few-Shot Visual Learning with Self-Supervision

Zero-Shot Semantic Segmentation

SoDeep: a Sorting Deep net to learn ranking loss surrogates

DADA: Depth-aware Domain Adaptation in Semantic Segmentation

FML: Face Model Learning from Videos

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision

Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events

A Flexible Convolutional Solver with Application to Photorealistic Style Transfer

Deep Video Portraits

Finding beans in burgers: Deep semantic-visual embedding with localization

Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz

Learning a Complete Image Indexing Pipeline

MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction