Yash Goyal

Email: yashgoyal.yg1-at-gmail.com

I am a Research Scientist at Samsung Advanced Institute of Technology (SAIT) AI Lab, Montreal.

Previously, I was a PhD student in the School of Interactive Computing at Georgia Tech. I was advised by Dhruv Batra. I also collaborated closely with Devi Parikh.

I used to co-organize the annual VQA Challenge.

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.

Email / CV / Google Scholar / arXiv

Research

Machine Learning, Explainable AI, Computer Vision, Vision & Language.

Publications

	Explaining Classifiers with Causal Concept Effect (CaCE) Yash Goyal, Amir Feder, Uri Shalit, Been Kim ArXiv, 2019
	Counterfactual Visual Explanations Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee International Conference on Machine Learning (ICML), 2019
	Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering Yash Goyal, Tejas Khot, Aishwarya Agrawal, Douglas Summers-Stay, Dhruv Batra, Devi Parikh International Journal of Computer Vision (IJCV), 2018 Project Website, Demo
	Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes Gordon Christie, Ankit Laddha, Aishwarya Agrawal, Stanislaw Antol, Yash Goyal, Kevin Kochersberger, Dhruv Batra equal contribution Computer Vision and Image Understanding (CVIU)*, 2017
	Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, Devi Parikh equal contribution Computer Vision and Pattern Recognition (CVPR)*, 2017 Project Website, Demo 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).
	Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes Gordon Christie, Ankit Laddha, Aishwarya Agrawal, Stanislaw Antol, Yash Goyal, Kevin Kochersberger, Dhruv Batra equal contribution 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.
	Towards Transparent AI Systems: Interpreting Visual Question Answering Models Yash Goyal, Akrit Mohapatra, Devi Parikh, Dhruv Batra 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.
	Yin and Yang: Balancing and Answering Binary Visual Questions Peng Zhang, Yash Goyal*, Douglas Summers-Stay, Dhruv Batra, Devi Parikh equal contribution Computer Vision and Pattern Recognition (CVPR), 2016 Data and Code 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!
	CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service Harsh Agrawal, Clint Solomon Mathialagan, Yash Goyal, Neelima Chavali, Prakriti Banik, Akrit Mohapatra, Ahmed Osman, Dhruv Batra Book Chapter, Mobile Cloud Visual Media Computing Editors: Gang Hua, Xian-Sheng Hua. Springer, 2015. Website We present a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.
	Design of a physiologically informed virtual reality based interactive platform for individuals with upper limb impairment Deepesh Kumar, Yash Goyal, Sunil Nair, Arvind Chauhan, Uttama Lahiri IEEE International System on Robot and Human Interactive Communication (Ro-MAN 2014), UK