4,674 research outputs found
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
Current methods for skeleton-based human action recognition usually work with
completely observed skeletons. However, in real scenarios, it is prone to
capture incomplete and noisy skeletons, which will deteriorate the performance
of traditional models. To enhance the robustness of action recognition models
to incomplete skeletons, we propose a multi-stream graph convolutional network
(GCN) for exploring sufficient discriminative features distributed over all
skeleton joints. Here, each stream of the network is only responsible for
learning features from currently unactivated joints, which are distinguished by
the class activation maps (CAM) obtained by preceding streams, so that the
activated joints of the proposed method are obviously more than traditional
methods. Thus, the proposed method is termed richly activated GCN (RA-GCN),
where the richly discovered features will improve the robustness of the model.
Compared to the state-of-the-art methods, the RA-GCN achieves comparable
performance on the NTU RGB+D dataset. Moreover, on a synthetic occlusion
dataset, the performance deterioration can be alleviated by the RA-GCN
significantly.Comment: Accepted by ICIP 2019, 5 pages, 3 figures, 3 table
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Medical Big Data Analysis in Hospital Information System
The rapidly increasing medical data generated from hospital information system (HIS) signifies the era of Big Data in the healthcare domain. These data hold great value to the workflow management, patient care and treatment, scientific research, and education in the healthcare industry. However, the complex, distributed, and highly interdisciplinary nature of medical data has underscored the limitations of traditional data analysis capabilities of data accessing, storage, processing, analyzing, distributing, and sharing. New and efficient technologies are becoming necessary to obtain the wealth of information and knowledge underlying medical Big Data. This chapter discusses medical Big Data analysis in HIS, including an introduction to the fundamental concepts, related platforms and technologies of medical Big Data processing, and advanced Big Data processing technologies
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition
Current methods for skeleton-based human action recognition usually work with
complete skeletons. However, in real scenarios, it is inevitable to capture
incomplete or noisy skeletons, which could significantly deteriorate the
performance of current methods when some informative joints are occluded or
disturbed. To improve the robustness of action recognition models, a
multi-stream graph convolutional network (GCN) is proposed to explore
sufficient discriminative features spreading over all skeleton joints, so that
the distributed redundant representation reduces the sensitivity of the action
models to non-standard skeletons. Concretely, the backbone GCN is extended by a
series of ordered streams which is responsible for learning discriminative
features from the joints less activated by preceding streams. Here, the
activation degrees of skeleton joints of each GCN stream are measured by the
class activation maps (CAM), and only the information from the unactivated
joints will be passed to the next stream, by which rich features over all
active joints are obtained. Thus, the proposed method is termed richly
activated GCN (RA-GCN). Compared to the state-of-the-art (SOTA) methods, the
RA-GCN achieves comparable performance on the standard NTU RGB+D 60 and 120
datasets. More crucially, on the synthetic occlusion and jittering datasets,
the performance deterioration due to the occluded and disturbed joints can be
significantly alleviated by utilizing the proposed RA-GCN.Comment: Accepted by IEEE T-CSVT, 11 pages, 6 figures, 10 table
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