8,636 research outputs found
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal coherence) or implicitly (e.g. by data augmentation). One idea that
has not, to date, been explored is the integration of latent variables which
permit a search over a learned space of transformations. Motivated by evidence
that people mentally simulate transformations in space while comparing
examples, so-called "mental rotation", we propose a transforming distance.
Here, a trained relational model actively transforms pairs of examples so that
they are maximally similar in some feature space yet respect the learned
transformational constraints. We apply our method to nearest-neighbour problems
on the Toronto Face Database and NORB
Techniques for effective and efficient fire detection from social media images
Social media could provide valuable information to support decision making in
crisis management, such as in accidents, explosions and fires. However, much of
the data from social media are images, which are uploaded in a rate that makes
it impossible for human beings to analyze them. Despite the many works on image
analysis, there are no fire detection studies on social media. To fill this
gap, we propose the use and evaluation of a broad set of content-based image
retrieval and classification techniques for fire detection. Our main
contributions are: (i) the development of the Fast-Fire Detection method
(FFDnR), which combines feature extractor and evaluation functions to support
instance-based learning, (ii) the construction of an annotated set of images
with ground-truth depicting fire occurrences -- the FlickrFire dataset, and
(iii) the evaluation of 36 efficient image descriptors for fire detection.
Using real data from Flickr, our results showed that FFDnR was able to achieve
a precision for fire detection comparable to that of human annotators.
Therefore, our work shall provide a solid basis for further developments on
monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise
Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian
Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano
Traina, 2015, Techniques for effective and efficient fire detection from
social media images, ICEIS, 34-4
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