Pixel-wise attribution methods largely leverage the backpropagation way to redistribute the prediction through the whole CNN model to single pixels. For example, Gradient (Simonyan et al., 2014) and Input*Gradient (Shrikumar et al., 2016) use gradient of the prediction w.r.t. each pixel as attribution scores; DeconvNet (Zeiler & Fergus, 2014) and Guided Backpropogation (Springenberg et al., 2015) employ well-designed operations on the gradients of nonlinear activation functions. However, the gradient saturation problem is inherent in the backpropagation way, which easily results in the vanishing gradients and underestimating importance of pixels. To solve this problem, DeepLIFT (Shrikumar et al., 2017) employs a baseline (reference) image to calculate the modified partial derivatives of the difference-from-reference as the importance of pixels; meanwhile, Integrated Gradients (IG) (Sundararajan et al., 2017) aggregates the gradients by gradually varying the input from the baseline to the original image. Despite great success, the significant pixels highlighted by pixel-wise attribution methods are easily spread out -- that is, the focus of the explanation model shifts into irrelevant edges, objects, or even background (see Figure 1, pixel-wise attribution methods select top 10% important pixels including snow and person for label dogsled). Region-level attribution methods combine single pixels into patches or regions, and mainly apply the perturbation mechanism to directly evaluate the marginal effect of each region by masking or replacing it. For example, LIME (Ribeiro et al., 2016) approximates the CNN function by a sparse linear model between the patches and prediction, which is learned on perturbations of patches. Prediction Difference Analysis (Zintgraf et al., 2017) replaces patches with a sample from other images and obtain the contribution of each pixel by averaging the importance of patches containing the pixel.