9,849 research outputs found
New Techniques in the Search for Z' Bosons and Other Neutral Resonances
The search for neutral resonances at the energy frontier has a long and
illustrious history, resulting in multiple discoveries. The canonical search
scans the reconstructed invariant mass distribution of identified fermion
pairs. Two recent analyses from the CDF experiment at the Fermilab Tevatron
have applied novel methods to resonance searches. One analysis uses simulated
templates to fit the inverse mass distribution of muon pairs, a quantity with
approximately constant resolution for momenta measured with a tracking
detector. The other analysis measures the angular distribution of electron
pairs as a function of dielectron mass, gaining sensitivity over a probe of the
mass spectrum alone. After reviewing several models that predict new neutral
resonances, we discuss these CDF analyses and potential future applications
PlaNet - Photo Geolocation with Convolutional Neural Networks
Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetation, road markings, and architectural details, which in
combination may allow one to determine an approximate location and occasionally
an exact location. Websites such as GeoGuessr and View from your Window suggest
that humans are relatively good at integrating these cues to geolocate images,
especially en-masse. In computer vision, the photo geolocation problem is
usually approached using image retrieval methods. In contrast, we pose the
problem as one of classification by subdividing the surface of the earth into
thousands of multi-scale geographic cells, and train a deep network using
millions of geotagged images. While previous approaches only recognize
landmarks or perform approximate matching using global image descriptors, our
model is able to use and integrate multiple visible cues. We show that the
resulting model, called PlaNet, outperforms previous approaches and even
attains superhuman levels of accuracy in some cases. Moreover, we extend our
model to photo albums by combining it with a long short-term memory (LSTM)
architecture. By learning to exploit temporal coherence to geolocate uncertain
photos, we demonstrate that this model achieves a 50% performance improvement
over the single-image model
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
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