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deficient human in the loop system for guiding dnn's attention co-occurrence bias among several feature elements such as boat and water leads to the feature that we want to extract often accompanied by some other features in the context ing to the characteristic of dnns we cannot expect a network trained by data sets with obvious biases to make decisions only based on the necessary features to address the co-occurrence bias the data needs some extra supervision existing annotation methods for such tasks mainly provide manually crafted polygon-based Target masks which provide accurate supervised data but are labor intensive to annotate almost all training data and enormous money required if The annotation operation is simplified to a simple mouse click and the data to be annotated is reduced from all training data to only the part of the data that needs to be annotated most by the user then we can significantly reduce annotation workload and save a lot of Labor and money therefo

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