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. In this paper, we propose to pay more attention to the characteristics of driving scenarios, lowering the difficulty of this tricky task. A training-free retrieval based method is thereby proposed to distinguish road obstacles from the surrounding road texture by computing the cosine similarity based on their appearance features, and significantly outperforms methods of the same category by around 20 percentage points. Besides, we find that there is a deep relation between our method and self-attention mechanism, and as a result a novel Transformer evolves from our retrieval based method, further boosting the performance.