Abhinav Moudgil

Visiting Scholar, Georgia Tech.

I am a Visiting Scholar at Georgia Tech, advised by Prof. Devi Parikh and Prof. Dhruv Batra. My current research focuses on building multi-modal embodied agents which can effectively navigate in a photo-realistic environment using visual and language cues.

I completed my Bachelors and Masters in Electronics and Communication Engineering (ECE) in 2019 at IIIT Hyderabad, where I pursued research in long-term visual object tracking with Prof. Vineet Gandhi. My Masters thesis is available here.

During my Masters, I also spent two wonderful semesters at UC San Diego and Stanford University in 2018. I worked with Prof. Sicun Gao at UC San Diego on sample efficient Reinforcement Learning algorithms for Atari games. At Stanford, I collaborated with Prof. Noah Goodman on recognizing and rating puns with a novel probabilistic model.

IIIT Hyderabad
2013-2019

GSOC, CERN
Summer 2016

UC San Diego
Summer 2018

STANFORD
Fall 2018

GEORGIA TECH
Jan 2020 - Present


News

Nov 2020 ConCAT accepted to the NeurIPS 2020 Self-Supervised Learning Workshop.
Oct 2020 Preprint out on arXiv: Contrast and Classify: Alternate Training for Robust VQA
Jan 2020 Joined Georgia Tech as a Research Scholar working with Prof. Devi Parikh and Prof. Dhruv Batra!
Dec 2019 Exploring 3Rs of Long-term Tracking accepted to WACV 2020.
Oct 2019 Graduated with Dual Degree (Bachelors + Masters) from IIIT Hyderabad!
Sep 2019 Defended my MS thesis on "Extending Visual Object Tracking for Long Time Horizons" (pdf).
Dec 2018 Oral presentation of Long-term Tracking Benchmark at ACCV 2018.
Oct 2018 Attended the first PyTorch Developer Conference (PTDC-2018) in San Francisco!
Aug 2018 Collaborating with Prof. Noah Goodman at Stanford University on probabilistic modelling of puns.
May 2018 Joined AI lab at UC San Diego as a Visiting Scholar with Prof. Sicun Gao!
Nov 2017 My project on Humor Generation got featured in a TED article!

Publications

Contrast and Classify: Alternate Training for Robust VQA

Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal

NeurIPS Self-Supervised Learning Workshop 2020


Exploring 3Rs of Long-term Tracking: Re-detection, Recovery and Reliability

Shyamgopal Karthik, Abhinav Moudgil, Vineet Gandhi

WACV 2020


Long-Term Visual Object Tracking Benchmark

Abhinav Moudgil, Vineet Gandhi

ACCV 2018 (Oral Presentation)


Open Source Projects


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pygoturn

Fast PyTorch implementation of visual tracker GOTURN (Held et al., ECCV 2016) which tracks an input object in a video at 100FPS with a deep siamese convolutional network.


Code Fork
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mosse-tracker

MATLAB implementation of MOSSE tracker (Bolme et al., CVPR 2010), which forms the basis for all the correlation filter-based object tracking algorithms.


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pun-model

Python implementation which reproduces results of the paper “A computational model of linguistic humor in puns” (Kao et al., CogSci 2015). It employs a probabilistic model to compute funniness rating for a given sentence.


Code Fork
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short-jokes-dataset

Collection of Python scripts for building Short Jokes dataset containing 231,657 jokes scraped from various websites like Reddit, Twitter etc.


Code Dataset Fork
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ai-bots

Implementation of various algorithms like Deep Q-learning, Policy Gradient, Simulated Annealing and Hill Climbing in Tensorflow / PyTorch; tested on OpenAI Gym environments.


Code Fork