470 research outputs found
Future Frame Prediction for Anomaly Detection -- A New Baseline
Anomaly detection in videos refers to the identification of events that do
not conform to expected behavior. However, almost all existing methods tackle
the problem by minimizing the reconstruction errors of training data, which
cannot guarantee a larger reconstruction error for an abnormal event. In this
paper, we propose to tackle the anomaly detection problem within a video
prediction framework. To the best of our knowledge, this is the first work that
leverages the difference between a predicted future frame and its ground truth
to detect an abnormal event. To predict a future frame with higher quality for
normal events, other than the commonly used appearance (spatial) constraints on
intensity and gradient, we also introduce a motion (temporal) constraint in
video prediction by enforcing the optical flow between predicted frames and
ground truth frames to be consistent, and this is the first work that
introduces a temporal constraint into the video prediction task. Such spatial
and motion constraints facilitate the future frame prediction for normal
events, and consequently facilitate to identify those abnormal events that do
not conform the expectation. Extensive experiments on both a toy dataset and
some publicly available datasets validate the effectiveness of our method in
terms of robustness to the uncertainty in normal events and the sensitivity to
abnormal events.Comment: IEEE Conference on Computer Vision and Pattern Recognition 201
Learning user-specific latent influence and susceptibility from information cascades
Predicting cascade dynamics has important implications for understanding
information propagation and launching viral marketing. Previous works mainly
adopt a pair-wise manner, modeling the propagation probability between pairs of
users using n^2 independent parameters for n users. Consequently, these models
suffer from severe overfitting problem, specially for pairs of users without
direct interactions, limiting their prediction accuracy. Here we propose to
model the cascade dynamics by learning two low-dimensional user-specific
vectors from observed cascades, capturing their influence and susceptibility
respectively. This model requires much less parameters and thus could combat
overfitting problem. Moreover, this model could naturally model
context-dependent factors like cumulative effect in information propagation.
Extensive experiments on synthetic dataset and a large-scale microblogging
dataset demonstrate that this model outperforms the existing pair-wise models
at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015
Simultaneous slack budgeting and retiming for synchronous circuits optimization
Abstract- With the challenges of growing functionality and scaling chip size, the possible performance improvements should be considered in the earlier IC design stages, which gives more freedom to the later optimization. Potential slack as an effective metric of possible performance improvements is considered in this work which, as far as we known, is the first work that maximizes the potential slack by retiming for synchronous sequential circuit. A simultaneous slack budgeting and incremental retiming algorithm is proposed for maximizing potential slack. The overall slack budget is optimized by relocating the FFs iteratively with the MIS-based slack estimation. Compared with the potential slack of a well-known min-period retiming, our algorithm improves potential slack averagely 19.6 % without degrading the circuit performance in reasonable runtime. Furthermore, at the expense of a small amount of timing performance, 0.52 % and 2.08%, the potential slack is increased averagely by 19.89 % and 28.16 % separately, which give a hint of the tradeoff between the timing performance and the slack budget.
An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Spatial Deep Features
Digital gigapixel whole slide image (WSI) is widely used in clinical
diagnosis, and automated WSI analysis is key for computer-aided diagnosis.
Currently, analyzing the integrated descriptor of probabilities or feature maps
from massive local patches encoded by ResNet classifier is the main manner for
WSI-level prediction. Feature representations of the sparse and tiny lesion
cells in cervical slides, however, are still challengeable for the
under-promoted upstream encoders, while the unused spatial representations of
cervical cells are the available features to supply the semantics analysis. As
well as patches sampling with overlap and repetitive processing incur the
inefficiency and the unpredictable side effect. This study designs a novel
inline connection network (InCNet) by enriching the multi-scale connectivity to
build the lightweight model named You Only Look Cytopathology Once (YOLCO) with
the additional supervision of spatial information. The proposed model allows
the input size enlarged to megapixel that can stitch the WSI without any
overlap by the average repeats decreased from to
for collecting features and predictions at two scales. Based on Transformer for
classifying the integrated multi-scale multi-task features, the experimental
results appear AUC score better and faster than the best
conventional method in WSI classification on multicohort datasets of 2,019
slides from four scanning devices.Comment: 16 pages, 8 figures, already submitted to Medical Image Analysi
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