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Chapter and Conference Paper
Adversarially Robust Panoptic Segmentation (ARPaS) Benchmark
We propose the Adversarially Robust Panoptic Segmentation (ARPaS) benchmark to assess the general robustness of panoptic segmentation techniques. To account for the differences between instance and semantic se...
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Article
The Open Images Dataset V4
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license...
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Chapter and Conference Paper
Connecting Vision and Language with Localized Narratives
We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse ove...
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Chapter and Conference Paper
Iterative Deep Retinal Topology Extraction
This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels. Building on top of a global model that performs a dense semantical classification of the pixels of the im...
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Chapter and Conference Paper
Deep Retinal Image Understanding
This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neura...
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Chapter and Conference Paper
Convolutional Oriented Boundaries
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is co...
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Article
From global image annotation to interactive object segmentation
This paper presents a graphical environment for the annotation of still images that works both at the global and local scales. At the global scale, each image can be tagged with positive, negative and neutral ...
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Chapter and Conference Paper
Supervised Assessment of Segmentation Hierarchies
This paper addresses the problem of the supervised assessment of hierarchical region-based image representations. Given the large amount of partitions represented in such structures, the supervised assessment ...
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Chapter and Conference Paper
ONN the Use of Neural Networks for Data Privacy
The need for data privacy motivates the development of new methods that allow to protect data minimizing the disclosure risk without losing valuable statistical information. In this paper, we propose a new pro...
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Chapter and Conference Paper
Improving Microaggregation for Complex Record Anonymization
Microaggregation is one of the most commonly employed microdata protection methods. This method builds clusters of at least k original records and replaces the records in each cluster with the centroid of the clu...
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Chapter and Conference Paper
Ordered Data Set Vectorization for Linear Regression on Data Privacy
Many situations demand from publishing data without revealing the confidential information in it. Among several data protection methods proposed in the literature, those based on linear regression are widely u...