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Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes.
A Study on Multimodal and Interactive Explanations for Visual Question Answering
Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation
For instance, if a model answers "red" to "What color is the balloon?
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
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.
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