Generating Visual Explanations
This addresses the need for interpretable AI in visual recognition, particularly for end-users requiring justification, though it is incremental as it builds on existing vision-language models.
The paper tackles the problem of generating visual explanations for classification decisions by proposing a model that jointly predicts a class label and provides a discriminative justification, achieving more discriminative explanations than existing captioning methods on a fine-grained bird species dataset.
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image aspects which justify visual predictions. We propose a new model that focuses on the discriminating properties of the visible object, jointly predicts a class label, and explains why the predicted label is appropriate for the image. We propose a novel loss function based on sampling and reinforcement learning that learns to generate sentences that realize a global sentence property, such as class specificity. Our results on a fine-grained bird species classification dataset show that our model is able to generate explanations which are not only consistent with an image but also more discriminative than descriptions produced by existing captioning methods.