DFT-CAM: DISCRETE FOURIER TRANSFORM DRIVEN CLASS ACTIVATION MAP

Class activation map methods (CAMs) serve one of the key roles in explainable artificial intelligence (XAI) and are recently being applied to weakly-supervised object localization, showing great potential in many applications. However, the current CAM methods still have room for improvement regarding the weakly-supervised object localization task. In this paper, we proposed DFT-CAM, a novel CAM method that combines discrete Fourier transform based feature encoding with an orthogonality-based feature selection scheme. DFT-CAM doesn’t require any training, better captures semantic information, aggregates only the most representative convolutional features, and improves the downstream tasks such as weakly-supervised object localization. The experimental results show promising performance compared to other CAM methods across different deep learning classification networks.