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Found 26 papers, 8 papers with code
Data-Driven Fast Frequency Control using Inverter-Based Resources
To address the control challenges associated with the increasing share of inverter-connected renewable energy resources, this paper proposes a direct data-driven approach for fast frequency control in the bulk power system.
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers.
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
In this paper, we propose a single-stage backbone network for Color-Event Unified Tracking (CEUTrack), which achieves the above functions simultaneously.
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.
Data-Driven Fast Frequency Control using Inverter-Based Resources
We develop and test a data-driven and area-based fast frequency control scheme, which rapidly redispatches inverter-based resources to compensate for local power imbalances within the bulk power system.
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection
Thus, the key problem of RGB-T SOD is to make the features from the two modalities complement and adjust each other flexibly, since it is inevitable that any modalities of RGB-T image pairs failure due to challenging scenes such as extreme light conditions and thermal crossover.
Noise and Edge Based Dual Branch Image Manipulation Detection
In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead of the original image to obtain more subtle traces of manipulation.
Prompt-based Learning for Unpaired Image Captioning
Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task.
Network Topology Optimization via Deep Reinforcement Learning
A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
1000x Faster Camera and Machine Vision with Ordinary Devices
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.
Ranked #1 on
Object Tracking
on VisEvent
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
NeuSpike-Net: High Speed Video Reconstruction via Bio-Inspired Neuromorphic Cameras
In this paper, we propose a NeuSpike-Net to learn both the high dynamic range and high motion sensitivity of DVS and the full texture sampling of spike camera to achieve high-speed and high dynamic image reconstruction.
GenAD: General Representations of Multivariate Time Series for Anomaly Detection
However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.
Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting
Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.
Retina-Like Visual Image Reconstruction via Spiking Neural Model
The experimental results show that the proposed approach is extremely effective in reconstructing the visual image in both normal and high speed scenes, while achieving high dynamic range and high image quality.
Algebraic Operations on Spatiotemporal Data Based on RDF
The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively.
A Retina-inspired Sampling Method for Visual Texture Reconstruction
Conventional frame-based camera is not able to meet the demand of rapid reaction for real-time applications, while the emerging dynamic vision sensor (DVS) can realize high speed capturing for moving objects.
HDI-Forest: Highest Density Interval Regression Forest
By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems.
Session-based Sequential Skip Prediction via Recurrent Neural Networks
The focus of WSDM cup 2019 is session-based sequential skip prediction, i. e. predicting whether users will skip tracks, given their immediately preceding interactions in their listening session.
A Domain Generalization Perspective on Listwise Context Modeling
As one of the most popular techniques for solving the ranking problem in information retrieval, Learning-to-rank (LETOR) has received a lot of attention both in academia and industry due to its importance in a wide variety of data mining applications.
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data.
Robust and Efficient Subspace Segmentation via Least Squares Regression
If the subspaces from which the data drawn are independent or orthogonal, they are able to obtain a block diagonal affinity matrix, which usually leads to a correct segmentation.
