LFI-CAM: Learning Feature Importance for Better Visual Explanation
This work addresses the need for more reliable and consistent visual explanations in computer vision, though it appears incremental as it builds on existing CAM methods.
The paper tackled the problem of improving both classification accuracy and visual explanation quality in CNNs by proposing LFI-CAM, a trainable architecture that learns feature importance to generate attention maps, resulting in outperforming baseline models in accuracy and significantly enhancing attention map quality and stability.
Class Activation Mapping (CAM) is a powerful technique used to understand the decision making of Convolutional Neural Network (CNN) in computer vision. Recently, there have been attempts not only to generate better visual explanations, but also to improve classification performance using visual explanations. However, the previous works still have their own drawbacks. In this paper, we propose a novel architecture, LFI-CAM, which is trainable for image classification and visual explanation in an end-to-end manner. LFI-CAM generates an attention map for visual explanation during forward propagation, at the same time, leverages the attention map to improve the classification performance through the attention mechanism. Our Feature Importance Network (FIN) focuses on learning the feature importance instead of directly learning the attention map to obtain a more reliable and consistent attention map. We confirmed that LFI-CAM model is optimized not only by learning the feature importance but also by enhancing the backbone feature representation to focus more on important features of the input image. Experimental results show that LFI-CAM outperforms the baseline models's accuracy on the classification tasks as well as significantly improves on the previous works in terms of attention map quality and stability over different hyper-parameters.