1,152 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
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
We make three contributions: (i) we present an evaluation of state-of-the-art
image representations for object category retrieval over standard benchmark
datasets containing 1M+ images; (ii) we show that ConvNets can be used to
obtain features which are incredibly performant, and yet much lower dimensional
than previous state-of-the-art image representations, and that their
dimensionality can be reduced further without loss in performance by
compression using product quantization or binarization. Consequently, features
with the state-of-the-art performance on large-scale datasets of millions of
images can fit in the memory of even a commodity GPU card; (iii) we show that
an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel
with downloading the new training images, allowing for a continuous refinement
of the model as more images become available, and simultaneous training and
ranking. The outcome is an on-the-fly system that significantly outperforms its
predecessors in terms of: precision of retrieval, memory requirements, and
speed, facilitating accurate on-the-fly learning and ranking in under a second
on a single GPU.Comment: Published in proceedings of ACCV 201
Mathematical modeling of blanched and unblanched solar dried ginger rhizome varieties.
This research examines the mathematical modelling of blanched and unblanched solar dried ginger rhizome varieties. The Umudike ginger I and II (UG I and UG II) were blanched with an Electric water bath in the Soil and Water Laboratory, Agricultural and Bioresources Engineering Department, Michael Okpara University of Agriculture Umudike, Abia State. The samples UG I and UG II, were blanched for 3, 6, and 9 minutes at 50℃ respectively. Each samples with the treatment were subjected to active solar drying in sequence. Also, blanched and unblanched UG I and UG II were subjected to active solar drying. The treatment was carried out at 10mm thickness for UG I and UG II rhizome. There were ten different mathematical drying models compared based on the correlation coefficient, mean bias error, root mean square error and reduced chi-square method. The various models used are efficient thin layer drying models and its best fitted model varies due to the blanched and unblanched treatments of UG I and UG II. It was also used to validate and predict equations for all the treatments. The Henderson and Pabis model was recommended for predicting the drying characteristics of blanched and unblanched UG I and UG II ginger rhizomes
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S, a lightweight, automated
approach for translating video recordings of Android app usages into replayable
scenarios. V2S is based primarily on computer vision techniques and adapts
recent solutions for object detection and image classification to detect and
classify user actions captured in a video, and convert these into a replayable
test scenario. We performed an extensive evaluation of V2S involving 175 videos
depicting 3,534 GUI-based actions collected from users exercising features and
reproducing bugs from over 80 popular Android apps. Our results illustrate that
V2S can accurately replay scenarios from screen recordings, and is capable of
reproducing 89% of our collected videos with minimal overhead. A case
study with three industrial partners illustrates the potential usefulness of
V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software
Engineering (ICSE'20), 13 page
Electron-pion separation in the ATLAS Tile hadron calorimeter
The ATLAS hadron Tile Calorimeter performance has been extensively studied during the test beam periods at the CERN SPS accelerator. The SPS beams contain the mixtures of the electrons, muons and pions, but for the physics studies it is important to deal with the pure beam species. Several methods of electron - pion separation were comparatively studied in this note, using available test beam data and detailed Monte Carlo simulation
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
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
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