  Visual Question Answering (VQA) is a challenging task of predicting the
answer to a question about the content of an image. It requires deep
understanding of both the textual question and visual image. Prior works
directly evaluate the answering models by simply calculating the accuracy of
the predicted answers. However, the inner reasoning behind the prediction is
disregarded in such a "black box" system, and we do not even know if one can
trust the predictions. In some cases, the models still get the correct answers
even when they focus on irrelevant visual regions or textual tokens, which
makes the models unreliable and illogical. To generate both visual and textual
rationales next to the predicted answer to the given image/question pair, we
propose Convincing Rationales for VQA, CRVQA. Considering the extra annotations
brought by the new outputs, {CRVQA} is trained and evaluated by samples
converted from some existing VQA datasets and their visual labels. The
extensive experiments demonstrate that the visual and textual rationales
support the prediction of the answers, and further improve the accuracy.
Furthermore, {CRVQA} achieves competitive performance on generic VQA datatsets
in the zero-shot evaluation setting. The dataset and source code will be
released under https://github.com/lik1996/CRVQA2024.
