161 research outputs found
Visual Tracking Using Max-Average Pooling and Weight-Selection Strategy
Many modern visual tracking algorithms incorporate spatial pooling, max pooling, or average pooling, which is to achieve invariance to feature transformations and better robustness to occlusion, illumination change, and position variation. In this paper, max-average pooling method and Weight-selection strategy are proposed with a hybrid framework, which is combined with sparse representation and particle filter, to exploit the spatial information of an object and make good compromises to ensure the correctness of the results in this framework. Challenges can be well considered by the proposed algorithm. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm compared with the state-of-the-art methods on challenging sequences
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or
item attributes to improve the performance of collaborative filtering-based
recommender systems. In this paper, the knowledge graph with user perception is
used to acquire the source of side information. We proposed KGUPN to address
the limitations of existing embedding-based and path-based knowledge
graph-aware recommendation methods, an end-to-end framework that integrates
knowledge graph and user awareness into scientific and technological news
recommendation systems. KGUPN contains three main layers, which are the
propagation representation layer, the contextual information layer and
collaborative relation layer. The propagation representation layer improves the
representation of an entity by recursively propagating embeddings from its
neighbors (which can be users, news, or relationships) in the knowledge graph.
The contextual information layer improves the representation of entities by
encoding the behavioral information of entities appearing in the news. The
collaborative relation layer complements the relationship between entities in
the news knowledge graph. Experimental results on real-world datasets show that
KGUPN significantly outperforms state-of-the-art baselines in scientific and
technological news recommendation
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
In this paper, we aim to devise a universally versatile style transfer method
capable of performing artistic, photo-realistic, and video style transfer
jointly, without seeing videos during training. Previous single-frame methods
assume a strong constraint on the whole image to maintain temporal consistency,
which could be violated in many cases. Instead, we make a mild and reasonable
assumption that global inconsistency is dominated by local inconsistencies and
devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local
patches. CCPL can preserve the coherence of the content source during style
transfer without degrading stylization. Moreover, it owns a neighbor-regulating
mechanism, resulting in a vast reduction of local distortions and considerable
visual quality improvement. Aside from its superior performance on versatile
style transfer, it can be easily extended to other tasks, such as
image-to-image translation. Besides, to better fuse content and style features,
we propose Simple Covariance Transformation (SCT) to effectively align
second-order statistics of the content feature with the style feature.
Experiments demonstrate the effectiveness of the resulting model for versatile
style transfer, when armed with CCPL.Comment: Accepted by ECCV2022 as an oral paper; code url:
https://github.com/JarrentWu1031/CCPL Video demo:
https://youtu.be/scZuJCXhL1
Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform
Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in image a priori operations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
The relation triples extraction method based on table filling can address the
issues of relation overlap and bias propagation. However, most of them only
establish separate table features for each relationship, which ignores the
implicit relationship between different entity pairs and different relationship
features. Therefore, a feature reasoning relational triple extraction method
based on table filling for technological patents is proposed to explore the
integration of entity recognition and entity relationship, and to extract
entity relationship triples from multi-source scientific and technological
patents data. Compared with the previous methods, the method we proposed for
relational triple extraction has the following advantages: 1) The table filling
method that saves more running space enhances the speed and efficiency of the
model. 2) Based on the features of existing token pairs and table relations,
reasoning the implicit relationship features, and improve the accuracy of
triple extraction. On five benchmark datasets, we evaluated the model we
suggested. The result suggest that our model is advanced and effective, and it
performed well on most of these datasets
Dynamic Fair Federated Learning Based on Reinforcement Learning
Federated learning enables a collaborative training and optimization of
global models among a group of devices without sharing local data samples.
However, the heterogeneity of data in federated learning can lead to unfair
representation of the global model across different devices. To address the
fairness issue in federated learning, we propose a dynamic q fairness federated
learning algorithm with reinforcement learning, called DQFFL. DQFFL aims to
mitigate the discrepancies in device aggregation and enhance the fairness of
treatment for all groups involved in federated learning. To quantify fairness,
DQFFL leverages the performance of the global federated model on each device
and incorporates {\alpha}-fairness to transform the preservation of fairness
during federated aggregation into the distribution of client weights in the
aggregation process. Considering the sensitivity of parameters in measuring
fairness, we propose to utilize reinforcement learning for dynamic parameters
during aggregation. Experimental results demonstrate that our DQFFL outperforms
the state-of-the-art methods in terms of overall performance, fairness and
convergence speed
Image Sequence Fusion and Denoising Based on 3D Shearlet Transform
We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects
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