1,086 research outputs found
Evaluating Two-Stream CNN for Video Classification
Videos contain very rich semantic information. Traditional hand-crafted
features are known to be inadequate in analyzing complex video semantics.
Inspired by the huge success of the deep learning methods in analyzing image,
audio and text data, significant efforts are recently being devoted to the
design of deep nets for video analytics. Among the many practical needs,
classifying videos (or video clips) based on their major semantic categories
(e.g., "skiing") is useful in many applications. In this paper, we conduct an
in-depth study to investigate important implementation options that may affect
the performance of deep nets on video classification. Our evaluations are
conducted on top of a recent two-stream convolutional neural network (CNN)
pipeline, which uses both static frames and motion optical flows, and has
demonstrated competitive performance against the state-of-the-art methods. In
order to gain insights and to arrive at a practical guideline, many important
options are studied, including network architectures, model fusion, learning
parameters and the final prediction methods. Based on the evaluations, very
competitive results are attained on two popular video classification
benchmarks. We hope that the discussions and conclusions from this work can
help researchers in related fields to quickly set up a good basis for further
investigations along this very promising direction.Comment: ACM ICMR'1
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as
ResNet-101 and Inception, benefiting from their powerful feature
representations but suffering from high computational costs. Conversely, some
lightweight model based detectors fulfil real time processing, while their
accuracies are often criticized. In this paper, we explore an alternative to
build a fast and accurate detector by strengthening lightweight features using
a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)
in human visual systems, we propose a novel RF Block (RFB) module, which takes
the relationship between the size and eccentricity of RFs into account, to
enhance the feature discriminability and robustness. We further assemble RFB to
the top of SSD, constructing the RFB Net detector. To evaluate its
effectiveness, experiments are conducted on two major benchmarks and the
results show that RFB Net is able to reach the performance of advanced very
deep detectors while keeping the real-time speed. Code is available at
https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201
Towards Bottom-Up Analysis of Social Food
in ACM Digital Health Conference 201
Single Shot Temporal Action Detection
Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-art methods adopt the "detection by classification" framework:
first do proposal, and then classify proposals. The main drawback of this
framework is that the boundaries of action instance proposals have been fixed
during the classification step. To address this issue, we propose a novel
Single Shot Action Detector (SSAD) network based on 1D temporal convolutional
layers to skip the proposal generation step via directly detecting action
instances in untrimmed video. On pursuit of designing a particular SSAD network
that can work effectively for temporal action detection, we empirically search
for the best network architecture of SSAD due to lacking existing models that
can be directly adopted. Moreover, we investigate into input feature types and
fusion strategies to further improve detection accuracy. We conduct extensive
experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When
setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD
significantly outperforms other state-of-the-art systems by increasing mAP from
19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201
The age of data-driven proteomics : how machine learning enables novel workflows
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges
Bose-Einstein Condensation of Helium and Hydrogen inside Bundles of Carbon Nanotubes
Helium atoms or hydrogen molecules are believed to be strongly bound within
the interstitial channels (between three carbon nanotubes) within a bundle of
many nanotubes. The effects on adsorption of a nonuniform distribution of tubes
are evaluated. The energy of a single particle state is the sum of a discrete
transverse energy Et (that depends on the radii of neighboring tubes) and a
quasicontinuous energy Ez of relatively free motion parallel to the axis of the
tubes. At low temperature, the particles occupy the lowest energy states, the
focus of this study. The transverse energy attains a global minimum value
(Et=Emin) for radii near Rmin=9.95 Ang. for H2 and 8.48 Ang.for He-4. The
density of states N(E) near the lowest energy is found to vary linearly above
this threshold value, i.e. N(E) is proportional to (E-Emin). As a result, there
occurs a Bose-Einstein condensation of the molecules into the channel with the
lowest transverse energy. The transition is characterized approximately as that
of a four dimensional gas, neglecting the interactions between the adsorbed
particles. The phenomenon is observable, in principle, from a singular heat
capacity. The existence of this transition depends on the sample having a
relatively broad distribution of radii values that include some near Rmin.Comment: 21 pages, 9 figure
Generic 3D Representation via Pose Estimation and Matching
Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
http://3drepresentation.stanford.edu/ and dataset website
https://github.com/amir32002/3D_Street_Vie
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Deep Learning for Single-Molecule Science
Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in Machine Learning (ML), so-called Deep Learning (DL) offers an interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional Machine Learning strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the 'internal workings' of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a Convolutional Neural Network, may be used for base calling in DNA sequencing applications. We compare it with a Support Vector Machine as a more conventional ML method, and and discuss some of the strengths and weaknesses of the approach. In particular, a 'deep' neural network has many features of a 'black box', which has important implications on how we look at and interpret data
Isotopic and spin selectivity of H_2 adsorbed in bundles of carbon nanotubes
Due to its large surface area and strongly attractive potential, a bundle of
carbon nanotubes is an ideal substrate material for gas storage. In addition,
adsorption in nanotubes can be exploited in order to separate the components of
a mixture. In this paper, we investigate the preferential adsorption of D_2
versus H_2(isotope selectivity) and of ortho versus para(spin selectivity)
molecules confined in the one-dimensional grooves and interstitial channels of
carbon nanotube bundles. We perform selectivity calculations in the low
coverage regime, neglecting interactions between adsorbate molecules. We find
substantial spin selectivity for a range of temperatures up to 100 K, and even
greater isotope selectivity for an extended range of temperatures,up to 300 K.
This isotope selectivity is consistent with recent experimental data, which
exhibit a large difference between the isosteric heats of D_2 and H_2 adsorbed
in these bundles.Comment: Paper submitted to Phys.Rev. B; 17 pages, 2 tables, 6 figure
Towards a resolution of the proton form factor problem: new electron and positron scattering data
There is a significant discrepancy between the values of the proton electric
form factor, , extracted using unpolarized and polarized electron
scattering. Calculations predict that small two-photon exchange (TPE)
contributions can significantly affect the extraction of from the
unpolarized electron-proton cross sections. We determined the TPE contribution
by measuring the ratio of positron-proton to electron-proton elastic scattering
cross sections using a simultaneous, tertiary electron-positron beam incident
on a liquid hydrogen target and detecting the scattered particles in the
Jefferson Lab CLAS detector. This novel technique allowed us to cover a wide
range in virtual photon polarization () and momentum transfer
() simultaneously, as well as to cancel luminosity-related systematic
errors. The cross section ratio increases with decreasing at . This measurement is consistent with the size of the form
factor discrepancy at GeV and with hadronic calculations
including nucleon and intermediate states, which have been shown to
resolve the discrepancy up to GeV.Comment: 6 pages, 4 figures, submitted to PR
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