  In anomaly segmentation for complex driving scenes, state-of-the-art
approaches utilize anomaly scoring functions to calculate anomaly scores. For
these functions, accurately predicting the logits of inlier classes for each
pixel is crucial for precisely inferring the anomaly score. However, in
real-world driving scenarios, the diversity of scenes often results in
distorted manifolds of pixel embeddings in embedding space. This effect is not
conducive to directly using the pixel embeddings for the logit prediction
during inference, a concern overlooked by existing methods. To address this
problem, we propose a novel method called Random Walk on Pixel Manifolds
(RWPM). RWPM utilizes random walks to reveal the intrinsic relationships among
pixels to refine the pixel embeddings. The refined pixel embeddings alleviate
the distortion of manifolds, improving the accuracy of anomaly scores. Our
extensive experiments show that RWPM consistently improve the performance of
the existing anomaly segmentation methods and achieve the best results. Code:
\url{https://github.com/ZelongZeng/RWPM}.
