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Found 12 papers, 5 papers with code
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
However, most existing FL works are not conducted on datasets collected from authentic IoT devices that capture unique modalities and inherent challenges of IoT data.
Adaptive Approximation-Based Control for Nonlinear Systems: A Unified Solution with Accurate and Inaccurate Measurements
A unified solution to adaptive approximation-based control for nonlinear systems with accurate and inaccurate state measurement is synthesized in this study.
Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems
The replay attack detection problem is studied from a new perspective based on parity space method in this paper.
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally.
Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives.
SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City
A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city.
Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing
In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task.
Ranked #1 on
Image Dehazing
on NH-HAZE
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation
Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.
Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems
It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are generated and used for the detection purpose.
