To address this challenge, we propose a multimodal teacher network based on a cross-modality attention-based fusion strategy to improve the segmentation accuracy by exploiting data from multiple modes.
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans.
We exceed the state-of-the-art results in all evaluations.
Machine learning-based medical anomaly detection is an important problem that has been extensively studied.
Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem.
Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains.
In addition, we demonstrate the practical implications of the proposed learning strategy, where the feedback path can be shared among multiple neural memory networks as a mechanism for knowledge sharing.
In this paper, we present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification.
The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance in several applications.
In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection.
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state.
This paper presents a novel framework for Speech Activity Detection (SAD).
Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.
Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content.
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling.
The goal of both GANs is to generate similar `action codes', a vector representation of the current action.
This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds.
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar).
With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments.
This paper presents a novel framework for automatic learning of complex strategies in human decision making.
We present a novel, complete deep learning framework for multi-person localisation and tracking.
Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these.
In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems.
We illustrate how a simple approximation of attention weights (i. e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours.