66 citations · 168 across the 35 of their papers we have counts for
6 papers · 1 filter
Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark
Pritish Sahu, Karan Sikka, Ajay Divakaran
We focus on Multimodal Machine Reading Comprehension (M3C) where a model is expected to answer questions based on given passage (or context), and the context and the questions can…
Comprehension Based Question Answering using Bloom's Taxonomy
Pritish Sahu, Michael Cogswell, Sara Rutherford-Quach +1
Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom's Taxonomy helps educators teach children how to use knowledge b…
Towards Solving Multimodal Comprehension
Pritish Sahu, Karan Sikka, Ajay Divakaran
This paper targets the problem of procedural multimodal machine comprehension (M3C). This task requires an AI to comprehend given steps of multimodal instructions and then answer q…
Confidence Calibration for Domain Generalization under Covariate Shift
Yunye Gong, Xiao Lin, Yi Yao +3
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they req…
Modular Adaptation for Cross-Domain Few-Shot Learning
Xiao Lin, Meng Ye, Yunye Gong +4
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples. While literature has demonstrated great successes via repr…
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…