328 research outputs found
Thinking about the Use of Electronic Information Engineering in Communication Intelligence
In the current stage, people's lives and jobs are frequently used with electronic mobile devices, subphones are a vehicle for communication technology, and humans receive information through communication technology, enabling the transmission of information to be completed. Loss, storage, and so forth, which makes it convenient for people to live and work daily, especially as they move in. When it comes to consumption, mobile payments embody the characteristics of safety and convenience that drive social and economic flight Rapid growth
RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic
segmentation in previously unseen domains with a limited number of annotated
samples. Although existing CD-FSS models focus on cross-domain feature
transformation, relying exclusively on inter-domain knowledge transfer may lead
to the loss of critical intra-domain information. To this end, we propose a
novel residual transformation network (RestNet) that facilitates knowledge
transfer while retaining the intra-domain support-query feature information.
Specifically, we propose a Semantic Enhanced Anchor Transform (SEAT) module
that maps features to a stable domain-agnostic space using advanced semantics.
Additionally, an Intra-domain Residual Enhancement (IRE) module is designed to
maintain the intra-domain representation of the original discriminant space in
the new space. We also propose a mask prediction strategy based on prototype
fusion to help the model gradually learn how to segment. Our RestNet can
transfer cross-domain knowledge from both inter-domain and intra-domain without
requiring additional fine-tuning. Extensive experiments on ISIC, Chest X-ray,
and FSS-1000 show that our RestNet achieves state-of-the-art performance. Our
code will be available soon.Comment: BMVC 202
Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning
In semi-supervised domain adaptation (SSDA), a few labeled target samples of
each class help the model to transfer knowledge representation from the fully
labeled source domain to the target domain. Many existing methods ignore the
benefits of making full use of the labeled target samples from multi-level. To
make better use of this additional data, we propose a novel Prototype-based
Multi-level Learning (ProML) framework to better tap the potential of labeled
target samples. To achieve intra-domain adaptation, we first introduce a
pseudo-label aggregation based on the intra-domain optimal transport to help
the model align the feature distribution of unlabeled target samples and the
prototype. At the inter-domain level, we propose a cross-domain alignment loss
to help the model use the target prototype for cross-domain knowledge transfer.
We further propose a dual consistency based on prototype similarity and linear
classifier to promote discriminative learning of compact target feature
representation at the batch level. Extensive experiments on three datasets,
including DomainNet, VisDA2017, and Office-Home demonstrate that our proposed
method achieves state-of-the-art performance in SSDA.Comment: IJCAI 202
StackVAE-G: An efficient and interpretable model for time series anomaly detection
Recent studies have shown that autoencoder-based models can achieve superior
performance on anomaly detection tasks due to their excellent ability to fit
complex data in an unsupervised manner. In this work, we propose a novel
autoencoder-based model, named StackVAE-G that can significantly bring the
efficiency and interpretability to multivariate time series anomaly detection.
Specifically, we utilize the similarities across the time series channels by
the stacking block-wise reconstruction with a weight-sharing scheme to reduce
the size of learned models and also relieve the overfitting to unknown noises
in the training data. We also leverage a graph learning module to learn a
sparse adjacency matrix to explicitly capture the stable interrelation
structure among multiple time series channels for the interpretable pattern
reconstruction of interrelated channels. Combining these two modules, we
introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph
neural network) model for multivariate time series anomaly detection. We
conduct extensive experiments on three commonly used public datasets, showing
that our model achieves comparable (even better) performance with the
state-of-the-art modelsand meanwhile requires much less computation and memory
cost. Furthermore, we demonstrate that the adjacency matrix learned by our
model accurately captures the interrelation among multiple channels, and can
provide valuable information for failure diagnosis applications.Comment: Accepted to AI Ope
TransSC: Transformer-based Shape Completion for Grasp Evaluation
Currently, robotic grasping methods based on sparse partial point clouds have
attained a great grasping performance on various objects while they often
generate wrong grasping candidates due to the lack of geometric information on
the object. In this work, we propose a novel and robust shape completion model
(TransSC). This model has a transformer-based encoder to explore more
point-wise features and a manifold-based decoder to exploit more object details
using a partial point cloud as input.
Quantitative experiments verify the effectiveness of the proposed shape
completion network and demonstrate it outperforms existing methods. Besides,
TransSC is integrated into a grasp evaluation network to generate a set of
grasp candidates. The simulation experiment shows that TransSC improves the
grasping generation result compared to the existing shape completion baselines.
Furthermore, our robotic experiment shows that with TransSC the robot is more
successful in grasping objects that are randomly placed on a support surface
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Time series anomaly detection (TSAD) is an important data mining task with
numerous applications in the IoT era. In recent years, a large number of deep
neural network-based methods have been proposed, demonstrating significantly
better performance than conventional methods on addressing challenging TSAD
problems in a variety of areas. Nevertheless, these deep TSAD methods typically
rely on a clean training dataset that is not polluted by anomalies to learn the
"normal profile" of the underlying dynamics. This requirement is nontrivial
since a clean dataset can hardly be provided in practice. Moreover, without the
awareness of their robustness, blindly applying deep TSAD methods with
potentially contaminated training data can possibly incur significant
performance degradation in the detection phase. In this work, to tackle this
important challenge, we firstly investigate the robustness of commonly used
deep TSAD methods with contaminated training data which provides a guideline
for applying these methods when the provided training data are not guaranteed
to be anomaly-free. Furthermore, we propose a model-agnostic method which can
effectively improve the robustness of learning mainstream deep TSAD models with
potentially contaminated data. Experiment results show that our method can
consistently prevent or mitigate performance degradation of mainstream deep
TSAD models on widely used benchmark datasets
An Analysis of Aesthetics in Makoto Shinkai's Animated Films
As a traditional Japanese literary concept, the concept of “mono no aware” gradually forms an aesthetic consciousness deeply rooted in Japanese culture through the interpretation and enrichment of local literature, poetry, film and television. This aesthetic consciousness is a series of melancholic and melancholic emotions that the aesthetic subject experiences towards the aesthetic object, as well as a pessimistic feeling towards the fleeting moments of life and the impermanence of time. It resonates with emotions, and this emotional expression is introverted and implicit. Even though there are waves in the heart, the surface is only a frown, revealing a faint emotion. This article is based on the analysis of Makoto Shinkai's animated films, and explores the common aesthetic ideas and realistic meaning. the conclusion is as follows: Makoto Shinkai combines traditional aesthetic ideas with modern computer technology to form a more delicate and resonant new unconscious aesthetic. This aesthetic is presented layer by layer through a series of life oriented themed animated films such as “regret”, “love”, and “distance”. It can better touch people's inner softness, comprehend the true essence of life, and awaken people's hope and motivation for life
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