Efficient Identification of Influential Image Patches using Game-Theoretic Interactions

The proposed method, MoXI, efficiently and accurately identifies a group of image patches that collectively have a high impact on the prediction confidence of an image classifier. MoXI leverages game-theoretic concepts of Shapley values and interactions to capture both the individual and cooperative contributions of image patches.