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Chapter and Conference Paper
How Stable Are Transferability Metrics Evaluations?
Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target dataset, without fine-tuning...
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Chapter and Conference Paper
The Missing Link: Finding Label Relations Across Datasets
Computer vision is driven by the many datasets available for training or evaluating novel methods. However, each dataset has a different set of class labels, visual definition of classes, images following a sp...
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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
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an image and a set of correction...
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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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Article
The Devil is in the Decoder: Classification, Regression and GANs
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consist of encoders which decrease...
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Chapter and Conference Paper
Region-Based Semantic Segmentation with End-to-End Training
We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing paradigms. Methods based on ...
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Chapter and Conference Paper
Daily Living Activities Recognition via Efficient High and Low Level Cues Combination and Fisher Kernel Representation
In this work we propose an efficient method for activity recognition in a daily living scenario. At feature level, we propose a method to extract and combine low- and high-level information and we show that th...
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Chapter and Conference Paper
Designing a Story Database for Use in Automatic Story Generation
In this paper we propose a model for the representation of stories in a story database. The use of such a database will enable computational story generation systems to learn from previous stories and associat...
