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Found 34 papers, 20 papers with code
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Towards Universal Representation for Unseen Action Recognition
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples.
Ranked #14 on
Action Recognition
on ActivityNet
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades.
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements).
Order Matters: Shuffling Sequence Generation for Video Prediction
Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community.
Dual-reference Age Synthesis
Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image.
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Instantiated rules contain constants extracted from KGs.
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts.
Fatigue Assessment using ECG and Actigraphy Sensors
For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment.
SOFA-Net: Second-Order and First-order Attention Network for Crowd Counting
To obtain the appropriate crowd representation, in this work we proposed SOFA-Net(Second-Order and First-order Attention Network): second-order statistics were extracted to retain selectivity of the channel-wise spatial information for dense heads while first-order statistics, which can enhance the feature discrimination for the heads' areas, were used as complementary information.
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function.
Alternating minimization algorithms for graph regularized tensor completion
The usage of graph regularization entails benefits in the learning accuracy of LRTC, but at the same time, induces coupling graph Laplacian terms that hinder the optimization of the tensor completion model.
Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers
Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels.
Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications.
Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization
In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization.
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor.
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
The task of skeleton-based action recognition remains a core challenge in human-centred scene understanding due to the multiple granularities and large variation in human motion.
Semi-Supervised Crowd Counting from Unlabeled Data
Our method achieved competitive performance in semi-supervised learning approaches on these crowd counting datasets.
MRI Reconstruction Using Deep Energy-Based Model
In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition
We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation.
Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing
Experimental results demonstrate important evidence that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep.
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition
To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc.
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.
A library of quantitative markers of seizure severity
In individual patients, 71% had a moderate to large difference (ranksum r > 0. 3) between focal and subclinical seizures in three or more markers.
Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics
To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems.
Action Quality Assessment with Temporal Parsing Transformer
Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition
In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions.
Universal Generative Modeling in Dual-domain for Dynamic MR Imaging
Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements.
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition
What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities.
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively.
Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction
In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image.
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction
Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the potential of the proposed method and the feasibility of achieving brain PET AC without CT.
