26 citations · 58 across the 5 of their papers we have counts for
9 papers
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…
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Meng Ye, Xiao Lin, Giedrius Burachas +2
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, repr…
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…
Training Deep Spiking Neural Networks
Eimantas Ledinauskas, Julius Ruseckas, Alfonsas Juršėnas +1
Computation using brain-inspired spiking neural networks (SNNs) with neuromorphic hardware may offer orders of magnitude higher energy efficiency compared to the current analog neu…
A Study on Multimodal and Interactive Explanations for Visual Question Answering
Kamran Alipour, Jurgen P. Schulze, Yi Yao +2
Explainability and interpretability of AI models is an essential factor affecting the safety of AI. While various explainable AI (XAI) approaches aim at mitigating the lack of tran…