Identifying Important Group of Pixels using Interactions

Kosuke Sumiyasu, Kazuhiko Kawamoto, Hiroshi Kera; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 6017-6026

To better understand the behavior of image classifiers it is useful to visualize the contribution of individual pixels to the model prediction. In this study we propose a method MoXI(Model eXplanation by Interactions) that efficiently and accurately identifies a group of pixels with high prediction confidence. The proposed method employs game-theoretic concepts Shapley values and interactions taking into account the effects of individual pixels and the cooperative influence of pixels on model confidence. Theoretical analysis and experiments demonstrate that our method better identifies the pixels that are highly contributing to the model outputs than widely-used by Grad-CAM Attention rollout and Shapley value. While prior studies have suffered from the exponential computational cost in the computation of Shapley value and interactions we show that this can be reduced to quadratic cost for our task. The code is available at https://github.com/KosukeSumiyasu/MoXI.