The anomaly segmentation via dynamic branch fusion

Anomaly segmentation is an important task in computer vision. At present, anomaly segmentation has achieved milestone development. Many representative works have been proposed, especially unsupervised learning methods and pre-training methods, but pre-training methods are difficult to solve cross-The gap brought by domains has not been paid much attention to in previous research work on how to mine the hidden information contained in the data itself. Because of the diversity of anomaly detection, it is crucial to make full use of the information of the object itself. This paper proposes a novel dynamic branch fusion structure, through mining the hidden information inside the data, so as to re-model more targeted abnormal segmentation. The method has been tested on two known benchmarks to verify the effectiveness of the proposed method.