  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
sometimes lead to incorrect anomaly predictions, and external reconstruction
models tend to be too inefficient for real-time self-driving embedded systems.
In this paper, we propose a new anomaly segmentation method, named pixel-wise
energy-biased abstention learning (PEBAL), that explores pixel-wise abstention
learning (AL) with a model that learns an adaptive pixel-level anomaly class,
and an energy-based model (EBM) that learns inlier pixel distribution. More
specifically, PEBAL is based on a non-trivial joint training of EBM and AL,
where EBM is trained to output high-energy for anomaly pixels (from outlier
exposure) and AL is trained such that these high-energy pixels receive adaptive
low penalty for being included to the anomaly class. We extensively evaluate
PEBAL against the SOTA and show that it achieves the best performance across
four benchmarks. Code is available at https://github.com/tianyu0207/PEBAL.
