Efficient Human-in-the-loop System for Guiding DNNs Attention

Understand

Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions.

• Focusing on image classification tasks, we propose an efficient human-in-the-loop system to interactively direct the attention of classifiers to the regions specified by users, thereby reducing the influence of co-occurrence bias and improving the transferability and interpretability of a DNN.
• Previous approaches for attention guidance require the preparation of pixel-level annotations and are not designed as interactive systems.
• We present a new interactive method to allow users to annotate images with simple clicks, and study a novel active learning strategy to significantly reduce the number of annotations.