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

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...

Full description

Saved in:
Bibliographic Details
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:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English

Similar Items