Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes

• Main Authors: TIAN, Yu, LIU, Yuyuan, PANG, Guansong, LIU, Fengbei, CHEN, Yuanhong, CARNEIRO, Gustavo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/7058; https://ink.library.smu.edu.sg/context/sis_research/article/8061/viewcontent/2111.12264.pdf
• Institution: Singapore Management University

State-of-the-art (SOTA) anomaly segmentation approaches on complex urban driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or external reconstruction models. However, previous uncertainty approaches that directly associate high uncertainty to anomaly may som...