26 citations · 60 across the 9 of their papers we have counts for
10 papers
Learning Invariant World State Representations with Predictive Coding
Avi Ziskind, Sujeong Kim, Giedrius T. Burachas
Self-supervised learning methods overcome the key bottleneck for building more capable AI: limited availability of labeled data. However, one of the drawbacks of self-supervised ar…
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…
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation
Arijit Ray, Karan Sikka, Ajay Divakaran +2
While models for Visual Question Answering (VQA) have steadily improved over the years, interacting with one quickly reveals that these models lack consistency. For instance, if a…
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
Arijit Ray, Yi Yao, Rakesh Kumar +2
While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting…
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
Shalini Ghosh, Giedrius Burachas, Arijit Ray +1
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 Answeri…