7,924 research outputs found
Semantic Photo Manipulation with a Generative Image Prior
Despite the recent success of GANs in synthesizing images conditioned on
inputs such as a user sketch, text, or semantic labels, manipulating the
high-level attributes of an existing natural photograph with GANs is
challenging for two reasons. First, it is hard for GANs to precisely reproduce
an input image. Second, after manipulation, the newly synthesized pixels often
do not fit the original image. In this paper, we address these issues by
adapting the image prior learned by GANs to image statistics of an individual
image. Our method can accurately reconstruct the input image and synthesize new
content, consistent with the appearance of the input image. We demonstrate our
interactive system on several semantic image editing tasks, including
synthesizing new objects consistent with background, removing unwanted objects,
and changing the appearance of an object. Quantitative and qualitative
comparisons against several existing methods demonstrate the effectiveness of
our method.Comment: SIGGRAPH 201
The Creation in Building Good News for The Society in Medan, Indonesia
the news has three news coverage techniques, namely reportage, interviews, library
research (literature studies). Some news writing techniques that can be a basic guide for
journalists are:The title of the news is as concise as possible with short and clear sentences,
but can still describe the core of the story as a whole.There are 5W + 1H elements.Arrange
news so that it can be presented with accurate, clear and interesting information. Use
language that is easily understood by readers from a variety of circles.Not "patronizing" but
"showing / presenting"
Generalization of form in visual pattern classification.
Human observers were trained to criterion in classifying compound Gabor signals with sym- metry relationships, and were then tested with each of 18 blob-only versions of the learning set. General- ization to dark-only and light-only blob versions of the learning signals, as well as to dark-and-light blob versions was found to be excellent, thus implying virtually perfect generalization of the ability to classify mirror-image signals. The hypothesis that the learning signals are internally represented in terms of a 'blob code' with explicit labelling of contrast polarities was tested by predicting observed generalization behaviour in terms of various types of signal representations (pixelwise, Laplacian pyramid, curvature pyramid, ON/OFF, local maxima of Laplacian and curvature operators) and a minimum-distance rule. Most representations could explain generalization for dark-only and light-only blob patterns but not for the high-thresholded versions thereof. This led to the proposal of a structure-oriented blob-code. Whether such a code could be used in conjunction with simple classifiers or should be transformed into a propo- sitional scheme of representation operated upon by a rule-based classification process remains an open question
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a
universal representation for recognition. However, global CNN activations lack
geometric invariance, which limits their robustness for classification and
matching of highly variable scenes. To improve the invariance of CNN
activations without degrading their discriminative power, this paper presents a
simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
This scheme extracts CNN activations for local patches at multiple scale
levels, performs orderless VLAD pooling of these activations at each level
separately, and concatenates the result. The resulting MOP-CNN representation
can be used as a generic feature for either supervised or unsupervised
recognition tasks, from image classification to instance-level retrieval; it
consistently outperforms global CNN activations without requiring any joint
training of prediction layers for a particular target dataset. In absolute
terms, it achieves state-of-the-art results on the challenging SUN397 and MIT
Indoor Scenes classification datasets, and competitive results on
ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets
The Petrie Museum of Egyptian Archaeology: Characters and Collections
The Petrie Museum of Egyptian Archaeology first opened its doors in 1915, and since then has attracted visitors from all over the world as well as providing valuable teaching resources. Named after its founder, the pioneering archaeologist Flinders Petrie, the Museum holds more than 80,000 objects and is one of the largest and finest collections of Egyptian and Sudanese archaeology in the world. Richly illustrated and engagingly written, the book moves back and forth between recent history and the ancient past, between objects and people. Experts discuss the discovery, history and care of key objects in the collections such as the Koptos lions and Roman era panel portraits. The rich and varied history of the Petrie Museum is revealed by the secrets that sit on its shelves
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Prior Learning and Gibbs Reaction-Diffusion
This article addresses two important themes in early visual computation: it presents a novel theory for learning the universal statistics of natural images, and, it proposes a general framework of designing reaction-diffusion equations for image processing. We studied the statistics of natural images including the scale invariant properties, then generic prior models were learned to duplicate the observed statistics, based on minimax entropy theory. The resulting Gibbs distributions have potentials of the form U(I; Λ, S)=Σα=1kΣx,yλ (α)((F(α)*I)(x,y)) with S={F(1) , F(2),...,F(K)} being a set of filters and Λ={λ(1)(),λ(2)(),...,λ (K)()} the potential functions. The learned Gibbs distributions confirm and improve the form of existing prior models such as line-process, but, in contrast to all previous models, inverted potentials were found to be necessary. We find that the partial differential equations given by gradient descent on U(I; Λ, S) are essentially reaction-diffusion equations, where the usual energy terms produce anisotropic diffusion, while the inverted energy terms produce reaction associated with pattern formation, enhancing preferred image features. We illustrate how these models can be used for texture pattern rendering, denoising, image enhancement, and clutter removal by careful choice of both prior and data models of this type, incorporating the appropriate featuresMathematic
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