2,272 research outputs found
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Decoupled Contrastive Multi-view Clustering with High-order Random Walks
In recent, some robust contrastive multi-view clustering (MvC) methods have
been proposed, which construct data pairs from neighborhoods to alleviate the
false negative issue, i.e., some intra-cluster samples are wrongly treated as
negative pairs. Although promising performance has been achieved by these
methods, the false negative issue is still far from addressed and the false
positive issue emerges because all in- and out-of-neighborhood samples are
simply treated as positive and negative, respectively. To address the issues,
we propose a novel robust method, dubbed decoupled contrastive multi-view
clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages
random walks to progressively identify data pairs in a global instead of local
manner. As a result, DIVIDE could identify in-neighborhood negatives and
out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC
architecture to perform inter- and intra-view contrastive learning in different
embedding spaces, thus boosting clustering performance and embracing the
robustness against missing views. To verify the efficacy of DIVIDE, we carry
out extensive experiments on four benchmark datasets comparing with nine
state-of-the-art MvC methods in both complete and incomplete MvC settings
Graph-guided Architecture Search for Real-time Semantic Segmentation
Designing a lightweight semantic segmentation network often requires
researchers to find a trade-off between performance and speed, which is always
empirical due to the limited interpretability of neural networks. In order to
release researchers from these tedious mechanical trials, we propose a
Graph-guided Architecture Search (GAS) pipeline to automatically search
real-time semantic segmentation networks. Unlike previous works that use a
simplified search space and stack a repeatable cell to form a network, we
introduce a novel search mechanism with new search space where a lightweight
model can be effectively explored through the cell-level diversity and
latencyoriented constraint. Specifically, to produce the cell-level diversity,
the cell-sharing constraint is eliminated through the cell-independent manner.
Then a graph convolution network (GCN) is seamlessly integrated as a
communication mechanism between cells. Finally, a latency-oriented constraint
is endowed into the search process to balance the speed and performance.
Extensive experiments on Cityscapes and CamVid datasets demonstrate that GAS
achieves the new state-of-the-art trade-off between accuracy and speed. In
particular, on Cityscapes dataset, GAS achieves the new best performance of
73.5% mIoU with speed of 108.4 FPS on Titan Xp.Comment: CVPR202
Cryptanalysis of A Privacy-Preserving Smart Metering Scheme Using Linkable Anonymous Credential
To accomplish effective privacy protection in smart grid systems, various approaches were proposed combining information security technology with the smart grid\u27s new features. Diao et al. proposed a privacy-preserving scheme using linkable anonymous credential based on CL signature, and demonstrated its identity anonymity, message authentication and traceability of broken smart meters. In this paper, a forgery attack is presented to point out the protocol dissatisfies message authentication and unforgeability. We hold the idea that this scheme doesn\u27t have basic safety requirements and application value
Molecular Beam Epitaxy Growth of Superconducting LiFeAs Film on SrTiO3(001) Substrate
The stoichiometric "111" iron-based superconductor, LiFeAs, has attacted
great research interest in recent years. For the first time, we have
successfully grown LiFeAs thin film by molecular beam epitaxy (MBE) on
SrTiO3(001) substrate, and studied the interfacial growth behavior by
reflection high energy electron diffraction (RHEED) and low-temperature
scanning tunneling microscope (LT-STM). The effects of substrate temperature
and Li/Fe flux ratio were investigated. Uniform LiFeAs film as thin as 3
quintuple-layer (QL) is formed. Superconducting gap appears in LiFeAs films
thicker than 4 QL at 4.7 K. When the film is thicker than 13 QL, the
superconducting gap determined by the distance between coherence peaks is about
7 meV, close to the value of bulk material. The ex situ transport measurement
of thick LiFeAs film shows a sharp superconducting transition around 16 K. The
upper critical field, Hc2(0)=13.0 T, is estimated from the temperature
dependent magnetoresistance. The precise thickness and quality control of
LiFeAs film paves the road of growing similar ultrathin iron arsenide films.Comment: 7 pages, 6 figure
Spin Speed and Supportedness Correlation and Evolution of Galaxy-Halo Systems
Galaxy angular momenta (spins) contain valuable cosmological information,
complementing with their positions and velocities. The baryonic spin direction
of galaxies have been probed as a reliable tracer of their host halos and the
primordial spin modes. Here we use the TNG100 simulation of the IllustrisTNG
project to study the spin magnitude correlations between dark matter, gas and
stellar components of galaxy-halo systems, and their evolutions across the
cosmic history. We find that these components generate similar initial spin
magnitudes from the same tidal torque in Lagrangian space. At low redshifts,
the gas component still traces the spin magnitude of dark matter halo and the
primordial spin magnitude. However, the traceability of stellar component
depends on the stellar mass fraction, . Our results
suggest that the galaxy baryonic spin magnitude can also serve as a tracer of
their host halo and the initial perturbations, and the similarity of their
evolution histories affects the galaxy-halo correlations.Comment: 9 pages, 7 figures, comments welcom
Combined finite element and multi-body dynamics analysis of effects of hydraulic cylinder movement on ploughshare of horizontally reversible plough
Abstract: Hydraulic Cylinder (HC), one of the key components of Horizontally Reversible Plough (HRP), takes the responsibilities for the commuting soiltillage of HRP. The dynamic behaviors of HC surely affectthe tilling performances of HRP. Based on our previously related work, this paper further addresses the effects of HC movements during tillage on ploughshare, especially at share-point, of HRP. For HC, uniform motion was considered in this study. A combined finite element and multi-body dynamics analysis (MDA) was implemented to assess both tillage kinematics and kinetics of the ploughshare. These numerical predictions were primarily involved in five different HC movement velocities and two actual HRP tilling scenarios, respectively, where loading data due to the HC movements were obtained from an MDA and applied to load a finite element modal of the ploughshare. Our results show that the importance of performing MDA as a preliminary step FEA to obtain an insight into the actual stress and strain variations at the share-point. Our findings demonstrate that the different movements of HC have no adverse effects on the service life of the ploughshare though they result in the maximum stress and strain at the sharepoint during HRP tillage
- …