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- Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition236
This paper proposes a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner and can achieve comparable or even superior performance to numerous supervised learning methods.
- 217
- Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal188
A WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues is proposed and a novel deep-learning model is designed that caters to the small-size WiFi activity data.
- 166
- Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning164
A Semantic Alignment Metric Learning (SAML) method for few-shot learning that aligns the semantically relevant dominant objects through a ``collect-and-select'' strategy and demonstrates the generalization ability of SAML and gives a theoretical guarantee.
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- A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications136
To facilitate sports analytics, a toolbox using PaddlePaddle is developed, which supports football, basketball, table tennis and figure skating action recognition, and discusses the challenges and unsolved problems in this area.
- Pairwise constraints based multiview features fusion for scene classification129
A novel multiview dimensionality reduction method for scene classification that takes both intraclass and interclass geometries into consideration, and has the best performance in scene classification.
- 124
- Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis123
The first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features is presented, and a feature loss is proposed to minimize the difference of high-level features between the generated and real images.
- Canonical correlation analysis networks for two-view image recognition123
A canonical correlation analysis network (CCANet) is presented to address image classification, in which images are represented by two-view features, and the experimental results demonstrate that the CCANet algorithm is more effective than PCANet, RandNet-1 andRandNet-2.
- Handbook on Advancements in Smart Antenna Technologies for Wireless Networks122
This uniquely comprehensive reference offers in-depth descriptions of terminologies, concepts, methods, and applications related to smart antennas in various wireless systems.
- Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks118
This paper proposes one lightweight action recognition architecture based on deep neural networks just using RGB data, consisting of convolution neural network, long short-term memory (LSTM) units, and temporal-wise attention model.
- Semantic preserving distance metric learning and applications116
A Semantic Preserving Distance Metric Learning (SP-DML) algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space to integrate semantic contents in distance metric learning.
- 115
- Mobile Cyber Physical Systems: Current Challenges and Future Networking Applications115
This paper distinguishes mobile CPS from traditional CPS, introduces their three emerging application areas, i.e., vehicular networking systems, healthcare systems, and mobile education, and considers the corresponding techniques, which may address these challenges, and analyze the inter-relations among them.
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- A Self-Supervised Gait Encoding Approach With Locality-Awareness for 3D Skeleton Based Person Re-Identification101
A self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person re-identification via gait features within 3D skeleton sequences and a locality-aware attention mechanism and contrastive learning scheme, which aim to preserve locality-awareness on intra-sequence level and inter- sequence level respectively during self- supervised learning.
- 100
- Multiview dimension reduction via Hessian multiset canonical correlations100
HesMCC is presented, which takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance of TCCA, KMUDA, MCCA and LapMCC for multiview dimension reduction.
- Skeleton embedded motion body partition for human action recognition using depth sequences95
An effective approach, which is insensitive to various temporal duration and adequate for complex background, for human action recognition using depth sequences, is proposed by embedding the skeleton information into depth maps by taking account of the geometrical structure of human body and contribute to the recognition task in complex background.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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