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Found 16 papers, 3 papers with code
Research on Splicing Image Detection Algorithms Based on Natural Image Statistical Characteristics
With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues.
Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment
This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism.
Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition
With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring.
A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product
This paper introduces a new multi-modal model based on the Transformer architecture and tensor product fusion strategy, combining BERT's text vectors and ViT's image vectors to classify students' psychological conditions, with an accuracy of 93. 65%.
Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems
This paper proposes a hierarchical compound intrinsic value reinforcement learning model -- innate-values-driven reinforcement learning termed IVRL to describe the complex behaviors of multi-agent interaction in their cooperation.
Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study in the Autism Spectrum Disorder Therapy
In recent years, edge computing has served as a paradigm that enables many future technologies like AI, Robotics, IoT, and high-speed wireless sensor networks (like 5G) by connecting cloud computing facilities and services to the end users.
Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey
As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests.
A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents
We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs.
A Strategy-Oriented Bayesian Soft Actor-Critic Model
Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability.
Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies
The needs describe the necessities for a system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior.
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning
Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability.
Learning Video Representations of Human Motion From Synthetic Data
In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement.
Self-Adaptive Swarm System (SASS)
Distributed artificial intelligence (DAI) studies artificial intelligence entities working together to reason, plan, solve problems, organize behaviors and strategies, make collective decisions and learn.
How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model
Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations.
Extension of causal decomposition in the mutual complex dynamic process
Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series.
Rotation Equivariant Graph Convolutional Network for Spherical Image Classification
Specifically, we propose a spherical graph construction criterion showing that a graph needs to be regular by evenly covering the spherical surfaces in order to design a rotation equivariant graph convolutional layer.
