1 citations · 1 across the 6 of their papers we have counts for
6 papers
Leveraging generative models to characterize the failure conditions of image classifiers
Adrien LeCoz, Stéphane Herbin, Faouzi Adjed
We address in this work the question of identifying the failure conditions of a given image classifier. To do so, we exploit the capacity of producing controllable distributions of…
Carpet-bombing patch: attacking a deep network without usual requirements
Pol Labarbarie, Adrien Chan-Hon-Tong, Stéphane Herbin +1
Although deep networks have shown vulnerability to evasion attacks, such attacks have usually unrealistic requirements. Recent literature discussed the possibility to remove or not…
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…
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…
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an a…
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ing…