Random Walk on Pixel Manifolds for Anomaly Segmentation of Complex Driving Scenes

arXiv:2404.17961v1 Announce Type: new
Abstract: 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, …

anomaly arxiv cs.cv driving pixel random segmentation type