1.4k citations · 1.5k across the 18 of their papers we have counts for
22 papers
Valeo4Cast: A Modular Approach to End-to-End Forecasting
Yihong Xu, Éloi Zablocki, Alexandre Boulch +11
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-t…
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen +2
Assessing the robustness of perception models to covariate shifts and their ability to detect out-of-distribution (OOD) inputs is crucial for safety-critical applications such as a…
A Simple Recipe for Language-guided Domain Generalized Segmentation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
Generalization to new domains not seen during training is one of the long-standing challenges in deploying neural networks in real-world applications. Existing generalization techn…
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Björn Michele, Alexandre Boulch, Gilles Puy +3
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case…
PØDA: Prompt-driven Zero-shot Domain Adaptation
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
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.…
DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning
Ivan Lopes, Tuan-Hung Vu, Raoul de Charette
Multi-task learning has recently emerged as a promising solution for a comprehensive understanding of complex scenes. In addition to being memory-efficient, multi-task models, when…
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
Antoine Saporta, Arthur Douillard, Tuan-Hung Vu +2
Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional…
CSG0: Continual Urban Scene Generation with Zero Forgetting
Himalaya Jain, Tuan-Hung Vu, Patrick Pérez +1
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…
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
Antoine Saporta, Tuan-Hung Vu, Matthieu Cord +1
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…
Semantic Palette: Guiding Scene Generation with Class Proportions
Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain +2
Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous…
Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette +2
Domain adaptation is an important task to enable learning when labels are scarce. While most works focus only on the image modality, there are many important multi-modal datasets.…
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…
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…
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette +2
Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In th…
Zero-Shot Semantic Segmentation
Maxime Bucher, Tuan-Hung Vu, Matthieu Cord +1
Semantic segmentation models are limited in their ability to scale to large numbers of object classes. In this paper, we introduce the new task of zero-shot semantic segmentation:…
DADA: Depth-aware Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2
Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it h…
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Object detection in video is crucial for many applications. Compared to images, video provides additional cues which can help to disambiguate the detection problem. Our goal in thi…
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2
Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks,…
Memory Warps for Learning Long-Term Online Video Representations
Tuan-Hung Vu, Wongun Choi, Samuel Schulter +1
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive. This is in contrast to prior works that often rely on computational…
Context-aware CNNs for person head detection
Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Person detection is a key problem for many computer vision tasks. While face detection has reached maturity, detecting people under a full variation of camera view-points, human po…