A Video Summarization Model Based on Deep Reinforcement Learning with Long-Term Dependency
X Wang, Y Li, H Wang, L Huang, S Ding - Sensors, 2022 - mdpi.com
Deep summarization models have succeeded in the video summarization field based on the
development of gated recursive unit (GRU) and long and short-term memory (LSTM) …
development of gated recursive unit (GRU) and long and short-term memory (LSTM) …
Susceptibility of continual learning against adversarial attacks
The recent advances in continual (incremental or lifelong) learning have concentrated on
the prevention of forgetting that can lead to catastrophic consequences, but there are two …
the prevention of forgetting that can lead to catastrophic consequences, but there are two …
Attribute-wise reasoning reinforcement learning for pedestrian attribute retrieval
Y Wang, Z Hu, Z Ji - International Journal of Multimedia Information …, 2023 - Springer
Pedestrian attribute retrieval (PAR) aims at retrieving soft-biometric attributes of pedestrian
images from video surveillance. Despite advancements, PAR grapples with challenges …
images from video surveillance. Despite advancements, PAR grapples with challenges …
Reinforcement and curriculum learning for off-road navigation of an UGV with a 3D LiDAR
M Sánchez, J Morales, JL Martínez - Sensors, 2023 - mdpi.com
This paper presents the use of deep Reinforcement Learning (RL) for autonomous
navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) …
navigation of an Unmanned Ground Vehicle (UGV) with an onboard three-dimensional (3D) …
Cryorl: Reinforcement learning enables efficient cryo-em data collection
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream
structural biology techniques because of its ability to determine high-resolution structures of …
structural biology techniques because of its ability to determine high-resolution structures of …
Graph convolution with topology refinement for automatic reinforcement learning
J Sang, Y Wang - Neurocomputing, 2023 - Elsevier
Reinforcement learning faces the challenge of sparse rewards. Existing research utilizes
reward shaping based on graph convolutional neural networks (GCNs) to address this …
reward shaping based on graph convolutional neural networks (GCNs) to address this …
Reinforced pedestrian attribute recognition with group optimization reward
Abstract Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video
surveillance. Two key challenges in PAR include complex alignment relations between …
surveillance. Two key challenges in PAR include complex alignment relations between …
Audio analysis speeding detection techniques based on metaheuristic-optimized machine learning models
Law enforcement plays an important role in road safety across the world. Speed
enforcement merits special attention as the correlation between exceeding speed limits and …
enforcement merits special attention as the correlation between exceeding speed limits and …
Simulation of flow field in silicon single-crystal growth using physics-informed neural network with spatial information
S Shi, D Liu, Z Huo - Physics of Fluids, 2022 - pubs.aip.org
Melt convection plays a crucial role in the growth of silicon single crystals. In particular, melt
flow transfers mass and heat, and it may strongly affect the crystal growth conditions …
flow transfers mass and heat, and it may strongly affect the crystal growth conditions …
A novel interactive deep cascade spectral graph convolutional network with multi-relational graphs for disease prediction
Graph neural networks (GNNs) have recently grown in popularity for disease prediction.
Existing GNN-based methods primarily build the graph topological structure around a single …
Existing GNN-based methods primarily build the graph topological structure around a single …