Evolving Into a Transformer: From a Training-Free Retrieval-Based Method for Anomaly Obstacle Segmentation.

As a key problem of auto-vehicle applications, the goal of Anomaly Obstacle Segmentation (AOS) is to detect some strange and unexpected obstacles (possibly are unseen previously) on the drivable area, thereby equipping the semantic perceptual model to be tolerant of unknown things. Due to its practicality, recently AOS is drawing attentions and a long line of works are proposed to tackle the obstacles with almost infinite diversity. However, these methods usually focus less on the priors of driving scenarios and involve image re-generation or the retraining of perceptual model, which lead to large computational quantity or the degradation of perceptual performance.