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Carpet-bombing patch: attacking a deep network without usual requirements
Although deep networks have shown vulnerability to evasion attacks, such attacks have usually unrealistic requirements.
Semantic bottleneck for computer vision tasks
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.
Generating Visual Representations for Zero-Shot Classification
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 by exemplar images as well.
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Zero-Shot learning has been shown to be an efficient strategy for domain adaptation.
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification.
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