Localization Focus
Localization focus in current research centers on accurately determining the position and orientation of objects or agents within various environments, ranging from robotic navigation to medical image analysis and multimedia forensics. Key research areas employ deep learning models, including convolutional neural networks, transformers, and graph neural networks, often combined with probabilistic methods and optimization techniques like Bayesian optimization or ADMM to improve accuracy and efficiency. These advancements are crucial for improving autonomous systems, enhancing medical diagnostics, and combating the spread of misinformation through advanced forgery detection and localization capabilities. The development of robust and efficient localization methods has significant implications across diverse scientific disciplines and practical applications.
Papers
A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO
Leon Fernando, Billy Pik Lik Lau, Chau Yuen, U-Xuan Tan
Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization
Fei Yu, Zhe Xiang, Nan Che, Zhuoran Zhang, Yuandi Li, Junxiao Xue, Zhiguo Wan
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
Zhenglin Huang, Jinwei Hu, Xiangtai Li, Yiwei He, Xingyu Zhao, Bei Peng, Baoyuan Wu, Xiaowei Huang, Guangliang Cheng
Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair
Qiong Feng, Xiaotian Ma, Jiayi Sheng, Ziyuan Feng, Wei Song, Peng Liang