155 research outputs found
Learning to Segment Every Thing
Most methods for object instance segmentation require all training examples
to be labeled with segmentation masks. This requirement makes it expensive to
annotate new categories and has restricted instance segmentation models to ~100
well-annotated classes. The goal of this paper is to propose a new partially
supervised training paradigm, together with a novel weight transfer function,
that enables training instance segmentation models on a large set of categories
all of which have box annotations, but only a small fraction of which have mask
annotations. These contributions allow us to train Mask R-CNN to detect and
segment 3000 visual concepts using box annotations from the Visual Genome
dataset and mask annotations from the 80 classes in the COCO dataset. We
evaluate our approach in a controlled study on the COCO dataset. This work is a
first step towards instance segmentation models that have broad comprehension
of the visual world
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
Refinement of retained austenite in super-bainitic steel by a deep cryogenic treatment
The effect of a deep cryogenic treatment on the microstructure of a super-bainitic steel was investigated. It was shown that quenching the super-bainitc steel in –196°C liquid nitrogen resulted in the transformation of retained austenite to two phases: ~20 nm thick martensite films and some nano carbides with a ~25 nm diameter. Some refinement of the retained austenite occurred, due to formation of fine martensite laths within the retained austenite. The evolution of these new phases resulted in an increase in the average hardness of the super-bainitic steel from 641 to ~670 HV1
Temporal Cross-Media Retrieval with Soft-Smoothing
Multimedia information have strong temporal correlations that shape the way
modalities co-occur over time. In this paper we study the dynamic nature of
multimedia and social-media information, where the temporal dimension emerges
as a strong source of evidence for learning the temporal correlations across
visual and textual modalities. So far, cross-media retrieval models, explored
the correlations between different modalities (e.g. text and image) to learn a
common subspace, in which semantically similar instances lie in the same
neighbourhood. Building on such knowledge, we propose a novel temporal
cross-media neural architecture, that departs from standard cross-media
methods, by explicitly accounting for the temporal dimension through temporal
subspace learning. The model is softly-constrained with temporal and
inter-modality constraints that guide the new subspace learning task by
favouring temporal correlations between semantically similar and temporally
close instances. Experiments on three distinct datasets show that accounting
for time turns out to be important for cross-media retrieval. Namely, the
proposed method outperforms a set of baselines on the task of temporal
cross-media retrieval, demonstrating its effectiveness for performing temporal
subspace learning.Comment: To appear in ACM MM 201
Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data
In most task and resting state fMRI studies, a group consensus is often sought, where individual variability is considered a nuisance. None the less, biological variability is an important factor that cannot be ignored and is gaining more attention in the field. One recent development is the individual identification based on static functional connectome. While the original work was based on the static connectome, subsequent efforts using recurrent neural networks (RNN) demonstrated that the inclusion of temporal features greatly improved identification accuracy. Given that convolutional RNN (ConvRNN) seamlessly integrates spatial and temporal features, the present work applied ConvRNN for individual identification with resting state fMRI data. Our result demonstrates ConvRNN achieving a higher identification accuracy than conventional RNN, likely due to better extraction of local features between neighboring ROIs. Furthermore, given that each convolutional output assembles in-place features, they provide a natural way for us to visualize the informative spatial pattern and temporal information, opening up a promising new avenue for analyzing fMRI data
Understanding the Eastward Shift and Intensification of the ENSO Teleconnection Over South Pacific and Antarctica Under Greenhouse Warming
The Pacific–South America (PSA) teleconnection pattern triggered by the El Niño/Southern Oscillation (ENSO) is suggested to be moving eastward and intensifying under global warming. However, the underlying mechanism is not completely understood. Previous studies have proposed that the movement of the PSA teleconnection pattern is attributable to the eastward shift of the tropical Pacific ENSO-driven rainfall anomalies in response to the projected El Niño-like sea surface temperature (SST) warming pattern. In this study, we found that with uniform warming, models will also simulate an eastward movement of the PSA teleconnection pattern, without the impact of the uneven SST warming pattern. Further investigation reveals that future changes in the climatology of the atmospheric circulation, particularly the movement of the exit region of the subtropical jet stream, can also contribute to the eastward shift of the PSA teleconnection pattern by modifying the conversion of mean kinetic energy to eddy kinetic energy
Strong enhancement of photoresponsivity with shrinking the electrodes spacing in few layer GaSe photodetectors
A critical challenge for the integration of the optoelectronics is that
photodetectors have relatively poor sensitivities at the nanometer scale. It is
generally believed that a large electrodes spacing in photodetectors is
required to absorb sufficient light to maintain high photoresponsivity and
reduce the dark current. However, this will limit the optoelectronic
integration density. Through spatially resolved photocurrent investigation, we
find that the photocurrent in metal-semiconductor-metal (MSM) photodetectors
based on layered GaSe is mainly generated from the photoexcited carriers close
to the metal-GaSe interface and the photocurrent active region is always close
to the Schottky barrier with higher electrical potential. The photoresponsivity
monotonically increases with shrinking the spacing distance before the direct
tunneling happen, which was significantly enhanced up to 5,000 AW-1 for the
bottom contacted device at bias voltage 8 V and wavelength of 410 nm. It is
more than 1,700-fold improvement over the previously reported results. Besides
the systematically experimental investigation of the dependence of the
photoresponsivity on the spacing distance for both the bottom and top contacted
MSM photodetectors, a theoretical model has also been developed to well explain
the photoresponsivity for these two types of device configurations. Our
findings realize shrinking the spacing distance and improving the performance
of 2D semiconductor based MSM photodetectors simultaneously, which could pave
the way for future high density integration of 2D semiconductor optoelectronics
with high performances.Comment: 25 pages, 4 figure
Excessively tilted fiber grating based Fe3O4 saturable absorber for passively mode-locked fiber laser
A novel approach to saturable absorber (SA) formation is presented by taking advantage of the mode coupling property of excessively tilted fiber grating (Ex-TFG). Stable mode-locked operation can be conveniently achieved based on the interaction between Ex- TFG coupled light and deposited ferroferric-oxide (Fe3O4) nanoparticles. The central wavelength, bandwidth and single pulse duration of the output are 1595 nm, 4.05 nm, and 912 fs, respectively. The fiber laser exhibits good long-term stability with signal-to-noise ratio (SNR) of 67 dB. For the first time, to the best of our knowledge, Ex-TFG based Fe3O4 SA for mode-locked fiber laser is demonstrated
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Multi-label image classification is a fundamental but challenging task
towards general visual understanding. Existing methods found the region-level
cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations
(i.e., object labels and bounding boxes) for effective learning of the
object-level visual features. In this paper, we propose a novel and efficient
deep framework to boost multi-label classification by distilling knowledge from
weakly-supervised detection task without bounding box annotations.
Specifically, given the image-level annotations, (1) we first develop a
weakly-supervised detection (WSD) model, and then (2) construct an end-to-end
multi-label image classification framework augmented by a knowledge
distillation module that guides the classification model by the WSD model
according to the class-level predictions for the whole image and the
object-level visual features for object RoIs. The WSD model is the teacher
model and the classification model is the student model. After this cross-task
knowledge distillation, the performance of the classification model is
significantly improved and the efficiency is maintained since the WSD model can
be safely discarded in the test phase. Extensive experiments on two large-scale
datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior
performances over the state-of-the-art methods on both performance and
efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table
Case report: A novel case of COVID-19 triggered tumefactive demyelinating lesions in one multiple sclerosis patient
The epidemic of COVID-19 is mainly manifested by respiratory symptoms caused by SARS-CoV-2 infection. Recently, reports of central nervous system diseases caused or aggravated by SARS-CoV-2 infection are also increasing. Thus, the COVID-19 pandemic poses an unprecedented challenge to the diagnosis and management of neurological disorders, especially to those diseases which have overlapping clinical and radiologic features with each other. In this study, a 31-year-old female patient had been diagnosed with relapsing–remitting multiple sclerosis (RRMS) initially and subsequently developed tumefactive demyelinating lesions (TDLs) following an infection with SARS-CoV-2. After immunotherapy (glucocorticoid pulses), a significant improvement was observed in her both clinical and radiological characteristics. The patient was started on disease-modifying therapy (DMT) with teriflunomide after cessation of oral glucocorticoids. Following two months of DMT treatment, the imaging follow-up revealed that the patient’s condition continued to deteriorate. This case was characterized by the transformation of a multiple sclerosis patient (MS) infected with SARS-CoV-2 into TDLs and the ineffectiveness of DMT treatment, which added complexity to its diagnosis and treatment. The case also gave us a hint that SARS-CoV-2 has a potential contributory role in inducing or exacerbating demyelinating diseases of the central nervous system that warrants further investigation
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