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Found 8 papers, 1 papers with code
Deep Federated Anomaly Detection for Multivariate Time Series Data
Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.
SEED: Sound Event Early Detection via Evidential Uncertainty
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.
Ordinal-Quadruplet: Retrieval of Missing Classes in Ordinal Time Series
In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i. e., during testing we can prescribe classes that are missing during training.
Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection
Next, a dual discriminator based adversarial training procedure, which jointly considers an image discriminator that can maintain the local consistency at frame-level and a video discriminator that can enforce the global coherence of temporal dynamics, is employed to enhance the future frame prediction.
3D Fusion of Infrared Images with Dense RGB Reconstruction from Multiple Views -- with Application to Fire-fighting Robots
This project integrates infrared and RGB imagery to produce dense 3D environment models reconstructed from multiple views.
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
Robust Landmark Detection for Alignment of Mouse Brain Section Images
In this work we describe the first steps in developing a semi-automated system to construct a histology at- las of mouse brainstem that combines atlas-guided annotation, landmark-based registration and atlas generation in an iterative framework.
The Active Atlas: Combining 3D Anatomical Models with Texture Detectors
We have developed a digital atlas methodology that combines information about the 3D organization of the brain and the detailed texture of neurons in different structures.
