13 citations · 31 across the 6 of their papers we have counts for
9 papers
SimVQA: Exploring Simulated Environments for Visual Question Answering
Paola Cascante-Bonilla, Hui Wu, Letao Wang +2
Existing work on VQA explores data augmentation to achieve better generalization by perturbing the images in the dataset or modifying the existing questions and answers. While thes…
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Jianbo Ouyang, Hui Wu, Min Wang +2
In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques.…
Separating Skills and Concepts for Novel Visual Question Answering
Spencer Whitehead, Hui Wu, Heng Ji +2
Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them…
Learning from Lexical Perturbations for Consistent Visual Question Answering
Spencer Whitehead, Hui Wu, Yi Ren Fung +3
Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modul…
Large Scale Neural Architecture Search with Polyharmonic Splines
Ulrich Finkler, Michele Merler, Rameswar Panda +8
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computationa…
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
Rameswar Panda, Michele Merler, Mayoore Jaiswal +8
Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existin…