  As deep learning models are increasingly used in safety-critical
applications, explainability and trustworthiness become major concerns. For
simple images, such as low-resolution face portraits, synthesizing visual
counterfactual explanations has recently been proposed as a way to uncover the
decision mechanisms of a trained classification model. In this work, we address
the problem of producing counterfactual explanations for high-quality images
and complex scenes. Leveraging recent semantic-to-image models, we propose a
new generative counterfactual explanation framework that produces plausible and
sparse modifications which preserve the overall scene structure. Furthermore,
we introduce the concept of "region-targeted counterfactual explanations", and
a corresponding framework, where users can guide the generation of
counterfactuals by specifying a set of semantic regions of the query image the
explanation must be about. Extensive experiments are conducted on challenging
datasets including high-quality portraits (CelebAMask-HQ) and driving scenes
(BDD100k). Code is available at https://github.com/valeoai/STEEX
