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The Image Similarity Challenge and data set for detecting image manipulation

#artificialintelligence

We also worked with trained third-party annotators to manually transform a smaller subset of the images to ensure we have even more selections representative of the way a human user would transform images. The annotators used image manipulation software GIMP to manually alter images in diverse ways that we cannot easily automate, for example handwriting or drawing on the images or cropping to leave only the part of the image most salient to the human eye. The Image Similarity Challenge invites participants to test their image matching techniques on the Image Similarity data set. More information for researchers is available here, and the accompanying paper is available here. For researchers considering attending NeurIPS 2021 in December, we're also pleased to announce that the Image Similarity Challenge has been accepted for the NeurIPS 2021 competition track, where we will be announcing the winners of this challenge (The competition is subject to official rules.


Learning Tversky Similarity

arXiv.org Machine Learning

In this paper, we advocate Tversky's ratio model as an appropriate basis for computational approaches to semantic similarity, that is, the comparison of objects such as images in a semantically meaningful way. We consider the problem of learning Tversky similarity measures from suitable training data indicating whether two objects tend to be similar or dissimilar. Experimentally, we evaluate our approach to similarity learning on two image datasets, showing that is performs very well compared to existing methods.


Similarity by Composition

Neural Information Processing Systems

We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal types.


Similarity by Composition

Neural Information Processing Systems

We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal types.


Similarity by Composition

Neural Information Processing Systems

We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal types.