As a research intern, I have spent time at Google Brain in spring+summer 2019,
Facebook AI Research in spring+summer 2017, at Army Research Laboratory (ARL) Adelphi in summer 2015, and at Duke University in summer 2013.
We counter the language priors present in the popular Visual Question Answering (VQA) dataset (Antol et al., ICCV 2015) and make vision (the V in VQA) matter! Specifically, we balance the VQA dataset by collecting complementary images such that every question in our balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the question. Our dataset is by construction more balanced than the original VQA dataset and has approximately twice the number of image-question pairs. Our complete balanced dataset will be publicly released as part of the 2nd iteration of the Visual Question Answering Challenge (VQA v2.0).
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016
We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images. We show that our vision and language modules have complementary strengths, and that joint reasoning produces more accurate results than any module operating in isolation.
International Conference on Machine Learning (ICML) Workshop on Visualization for Deep Learning, 2016
[Best Student Paper]
Interactive Visualizations: Question and Image
In this paper, we experimented with two visualization methods -- guided backpropagation and occlusion -- to interpret deep learning models for the task of Visual Question Answering. Specifically, we find what part of the input (pixels in images or words in questions) the VQA model focuses on while answering a question about an image.
We balance the existing VQA dataset so that VQA models are forced to understand the image to improve their performance. We propose an approach that focuses heavily on vision and answers the question by visual verification. Dataset and Code will be available soon!