5 citations · 10 across the 11 of their papers we have counts for
12 papers
HSTGFormer: Hyper Spatial-Temporal Graph Transformer for 3D Human Pose Estimation
Ruochen Li, Shuang Chen, Wenke E +2
Transformer-based methods have achieved strong performance in monocular 3D human pose estimation, but most existing approaches organise spatial and temporal reasoning as separate s…
CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection
Hanadi Alhamdan, Ghadah Alosaimi, Amir Atapour-Abarghouei +1
Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promising results in detecting risky d…
UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement
Shuang Chen, Ruochen Li, Zihan Zhu +3
Underwater image enhancement (UIE) is a practically important yet underexplored application of spiking neural networks (SNNs), where the dominant degradations are large-scale and l…
Motion-Adaptive Multi-Scale Temporal Modelling with Skeleton-Constrained Spatial Graphs for Efficient 3D Human Pose Estimation
Ruochen Li, Shuang Chen, Wenke E +2
Accurate 3D human pose estimation from monocular videos requires effective modelling of complex spatial and temporal dependencies. However, existing methods often face challenges i…
Exploring the Potentials of Spiking Neural Networks for Image Deraining
Shuang Chen, Tomas Krajnik, Farshad Arvin +1
Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining…
DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics
Shuang Chen, Ronald Thenius, Farshad Arvin +1
Continuous and reliable underwater monitoring is essential for assessing marine biodiversity, detecting ecological changes and supporting autonomous exploration in aquatic environm…
Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
Shuang Chen, Yifeng He, Barry Lennox +2
Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cos…
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms
Seongin Na, Tomáš Rouček, Jiří Ulrich +4
The recent advancement of Deep Reinforcement Learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach f…
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
Hanlin Niu, Ze Ji, Farshad Arvin +3
This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unkno…
Cooperative Pollution Source Localization and Cleanup with a Bio-inspired Swarm Robot Aggregation
Arash S. Amjadi, Mohsen Raoufi, Ali E. Turgut +3
Using robots for exploration of extreme and hazardous environments has the potential to significantly improve human safety. For example, robotic solutions can be deployed to find t…
Self-organized Collective Motion with a Simulated Real Robot Swarm
Mohsen Raoufi, Ali Emre Turgut, Farshad Arvin
Collective motion is one of the most fascinating phenomena observed in the nature. In the last decade, it aroused so much attention in physics, control and robotics fields. In part…
Omnipotent Virtual Giant for Remote Human-Swarm Interaction
Inmo Jang, Junyan Hu, Farshad Arvin +2
This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environm…