34,691 research outputs found
Efficient Blind-Spot Neural Network Architecture for Image Denoising
Image denoising is an essential tool in computational photography. Standard
denoising techniques, which use deep neural networks at their core, require
pairs of clean and noisy images for its training. If we do not possess the
clean samples, we can use blind-spot neural network architectures, which
estimate the pixel value based on the neighbouring pixels only. These networks
thus allow training on noisy images directly, as they by-design avoid trivial
solutions. Nowadays, the blind-spot is mostly achieved using shifted
convolutions or serialization. We propose a novel fully convolutional network
architecture that uses dilations to achieve the blind-spot property. Our
network improves the performance over the prior work and achieves
state-of-the-art results on established datasets
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201
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