Search Results for author: Tuan-Hung Vu

Found 18 papers, 12 papers with code

PØDA: Prompt-driven Zero-shot Domain Adaptation

1 code implementation6 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

Cross-task Attention Mechanism for Dense Multi-task Learning

1 code implementation17 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.

2D Semantic Segmentation Multi-Task Learning +4

Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

1 code implementation25 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.

Continual Learning Semantic Segmentation +2

CSG0: Continual Urban Scene Generation with Zero Forgetting

no code implementations6 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.

Continual Learning Scene Generation +1

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

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.

Semantic Segmentation Transfer Learning +1

Semantic Palette: Guiding Scene Generation with Class Proportions

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.

Data Augmentation Image Generation +1

Confidence Estimation via Auxiliary Models

no code implementations11 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).

Domain Adaptation Image Classification +1

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

no code implementations10 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.

Autonomous Vehicles Motion Planning +1

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

1 code implementation15 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.

Self-Supervised Learning Semantic Segmentation +1

Handling new target classes in semantic segmentation with domain adaptation

no code implementations2 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.

Scene Segmentation Universal Domain Adaptation +2

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

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.

3D Semantic Segmentation Autonomous Driving +1

Tube-CNN: Modeling temporal evolution of appearance for object detection in video

no code implementations6 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.

object-detection Object Detection +1

Memory Warps for Learning Long-Term Online Video Representations

no code implementations28 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.

object-detection Object Detection

Context-aware CNNs for person head detection

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.

Face Detection Head Detection +1

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