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:
https://github.com/ZelongZeng/RWPM.

在复杂的驾驶场景的异常分割中，我们提出了一种名为随机游走像素流形（RWPM）的新方法，通过利用随机游走来改善像素嵌入，从而改善异常评分的准确性，经过广泛实验证明，RWPM 持续改善现有的异常分割方法的性能并取得最佳结果。