Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes.
Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
For instance, if a model answers "red" to "What color is the balloon?
Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.
In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i. e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem.