26 citations · 55 across the 8 of their papers we have counts for
14 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…
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
Xiaoyu Lin, Deblina Bhattacharjee, Majed El Helou +1
Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors…
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…
Learning degraded image classification with restoration data fidelity
Xiaoyu Lin
Learning-based methods especially with convolutional neural networks (CNN) are continuously showing superior performance in computer vision applications, ranging from image classif…