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
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko, Christopher Tessum
Localization, balance and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images
Yakun Xie, Suning Liu, Hongyu Chen, Shaohan Cao, Huixin Zhang, Dejun Feng, Qian Wan, Jun Zhu, Qing Zhu
Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization
Shimin Chen, Wei Li, Jianyang Gu, Chen Chen, Yandong Guo
Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization
Xiao Guo, Xiaohong Liu, Iacopo Masi, Xiaoming Liu