1 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
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…
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…
Semantic bottleneck for computer vision tasks
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. M…
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,…
Generating Visual Representations for Zero-Shot Classification
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined…