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
Localization in Dynamic Planar Environments Using Few Distance Measurements
Michael M. Bilevich, Shahar Guini, Dan Halperin
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Hao Chen, Deyin Liu, Chunhua Shen
Recompression Based JPEG Tamper Detection and Localization Using Deep Neural Network Eliminating Compression Factor Dependency
Jamimamul Bakas, Praneta Rawat, Kalyan Kokkalla, Ruchira Naskar
ELSA: Evaluating Localization of Social Activities in Urban Streets
Maryam Hosseini, Marco Cipriano, Sedigheh Eslami, Daniel Hodczak, Liu Liu, Andres Sevtsuk, Gerard de Melo
DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention
Yang Liu, Xiaofei Li, Jun Zhang, Shengze Hu, Jun Lei