1 code implementation • 6 Dec 2022 • Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette
Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions.
Prompt-driven Zero-shot Domain Adaptation Semantic Segmentation +2
1 code implementation • 17 Jun 2022 • Ivan Lopes, Tuan-Hung Vu, Raoul de Charette
Multi-task learning has recently become a promising solution for a comprehensive understanding of complex scenes.
Ranked #58 on Semantic Segmentation on NYU Depth v2
1 code implementation • 25 Apr 2022 • Antoine Saporta, Arthur Douillard, Tuan-Hung Vu, Patrick Pérez, Matthieu Cord
Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain.
no code implementations • 6 Dec 2021 • Himalaya Jain, Tuan-Hung Vu, Patrick Pérez, Matthieu Cord
With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving.
1 code implementation • ICCV 2021 • Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time.
1 code implementation • CVPR 2021 • Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain, Patrick Pérez, Matthieu Cord
Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem.
2 code implementations • 18 Jan 2021 • Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, Patrick Pérez
Domain adaptation is an important task to enable learning when labels are scarce.
no code implementations • 11 Dec 2020 • Charles Corbière, Nicolas Thome, Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).
no code implementations • 10 Jul 2020 • Adithya Ranga, Filippo Giruzzi, Jagdish Bhanushali, Emilie Wirbel, Patrick Pérez, Tuan-Hung Vu, Xavier Perrotton
In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences.
1 code implementation • 15 Jun 2020 • Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
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 weather conditions for instance.
no code implementations • 2 Apr 2020 • Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
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.
1 code implementation • CVPR 2020 • Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Émilie Wirbel, Patrick Pérez
In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation.
2 code implementations • NeurIPS 2019 • Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
Semantic segmentation models are limited in their ability to scale to large numbers of object classes.
Ranked #1 on Zero-Shot Learning on PASCAL Context
2 code implementations • ICCV 2019 • Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
As a result, the performance of the trained semantic segmentation model on the target domain is boosted.
Ranked #17 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
no code implementations • 6 Dec 2018 • Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Our goal in this paper is to learn discriminative models for the temporal evolution of object appearance and to use such models for object detection.
3 code implementations • CVPR 2019 • Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
Semantic segmentation is a key problem for many computer vision tasks.
Ranked #19 on Image-to-Image Translation on GTAV-to-Cityscapes Labels
no code implementations • 28 Mar 2018 • Tuan-Hung Vu, Wongun Choi, Samuel Schulter, Manmohan Chandraker
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive.
1 code implementation • ICCV 2015 • Tuan-Hung Vu, Anton Osokin, Ivan Laptev
First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image.