  Deep models are the defacto standard in visual decision models due to their
impressive performance on a wide array of visual tasks. However, they are
frequently seen as opaque and are unable to explain their decisions. In
contrast, humans can justify their decisions with natural language and point to
the evidence in the visual world which led to their decisions. We postulate
that deep models can do this as well and propose our Pointing and Justification
(PJ-X) model which can justify its decision with a sentence and point to the
evidence by introspecting its decision and explanation process using an
attention mechanism. Unfortunately there is no dataset available with reference
explanations for visual decision making. We thus collect two datasets in two
domains where it is interesting and challenging to explain decisions. First, we
extend the visual question answering task to not only provide an answer but
also a natural language explanation for the answer. Second, we focus on
explaining human activities which is traditionally more challenging than object
classification. We extensively evaluate our PJ-X model, both on the
justification and pointing tasks, by comparing it to prior models and ablations
using both automatic and human evaluations.
