  The recent emerged weakly supervised object localization (WSOL) methods can
learn to localize an object in the image only using image-level labels.
Previous works endeavor to perceive the interval objects from the small and
sparse discriminative attention map, yet ignoring the co-occurrence confounder
(e.g., bird and sky), which makes the model inspection (e.g., CAM) hard to
distinguish between the object and context. In this paper, we make an early
attempt to tackle this challenge via causal intervention (CI). Our proposed
method, dubbed CI-CAM, explores the causalities among images, contexts, and
categories to eliminate the biased co-occurrence in the class activation maps
thus improving the accuracy of object localization. Extensive experiments on
several benchmarks demonstrate the effectiveness of CI-CAM in learning the
clear object boundaries from confounding contexts. Particularly, in
CUB-200-2011 which severely suffers from the co-occurrence confounder, CI-CAM
significantly outperforms the traditional CAM-based baseline (58.39% vs 52.4%
in top-1 localization accuracy). While in more general scenarios such as
ImageNet, CI-CAM can also perform on par with the state of the arts.
