8 citations · 20 across the 5 of their papers we have counts for
4 papers · 1 filter
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Kamran Alipour, Aditya Lahiri, Ehsan Adeli +2
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases. Counterfact…
Improving Users' Mental Model with Attention-directed Counterfactual Edits
Kamran Alipour, Arijit Ray, Xiao Lin +4
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems ans…
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Arijit Ray, Michael Cogswell, Xiao Lin +4
Attention maps, a popular heatmap-based explanation method for Visual Question Answering (VQA), are supposed to help users understand the model by highlighting portions of the imag…
The Impact of Explanations on AI Competency Prediction in VQA
Kamran Alipour, Arijit Ray, Xiao Lin +3
Explainability is one of the key elements for building trust in AI systems. Among numerous attempts to make AI explainable, quantifying the effect of explanations remains a challen…