Yash Kant

Hi! I am a Ph.D. student in Department of Computer Science and Robotics Group at University of Toronto. I am advised by Igor Gilitschenski.

I am working at Snap Research in Sergey Tulyakov's team. Here, I built a fast and data-driven character animation technique, and currently I am trying to build 3D neural representations using generative networks.

Previously, I was a Research Visitor at Georgia Tech advised by Devi Parikh and Dhruv Batra for two years. There, I built Visual Question Answering models that can read and are robust, and a benchmark to measure commonsense in embodied AI agents.

I enjoy talking to people and building (hopefully useful) things together. :)

Email / CV / Github / Google Scholar / Twitter / LinkedIn

Highlights

Co-created and TA'd a 3D Vision course with Igor in Winter 2022!
I am reviewing/reviewed for CVPR[21,22,23], ECCV[22], ICCV[21,23], AAAI[23]
Lead organizer of TextVQA and co-organizer of TextCaps and VQA challenges at CVPR 2021!
TA'd' Introduction to Mobile Robotics w/ Florian Shkurti in Fall 2021!

Research
	Invertible Neural Skinning Yash Kant, Aliaksandr Siarohin, Riza Alp Guler, Menglei Chai, Jian Ren, Sergey Tulyakov, Igor Gilitschenski CVPR, 2023 arXiv / project page We propose an end-to-end invertible and learnable reposing pipeline that allows animating implicit surfaces with intricate pose-varying effects. We outperform the state-of-the-art reposing techniques on clothed humans while preserving surface correspondences and being order of magnitude faster!
	Building Scalable Video Understanding Benchmarks through Sports Aniket Agarwal^, Alex Zhang^, Karthik Narasimhan, Igor Gilitschenski, Vishvak Murahari, Yash Kant arXiv / project page / code We introduce an automated Annotation and Video Stream Alignment Pipeline (abbreviated ASAP) for aligning unlabeled videos of four different sports (Cricket, Football, Basketball, and American Football) with their corresponding dense annotations (commentary) freely available on the web. Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed!
	LaTeRF: Label and Text Driven Object Radiance Fields Ashkan Mirzaei, Yash Kant, Jonathan Kelly, and Igor Gilitschenski ECCV, 2022 arXiv / code We build a simple method to extract an object from a scene given 2D images, camera poses, a natural language description of the object, and a few annotated pixels of object and background.
	Housekeep: Tidying Virtual Households using Commonsense Reasoning Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, and Harsh Agrawal ECCV, 2022 arXiv / project page / code / colab Housekeep is a benchmark to evaluate commonsense reasoning in the home for embodied AI. Here, an embodied agent must tidy a house by rearranging misplaced objects without explicit instructions. To capture the rich diversity of real world scenarios, we support cluttering environments with ~1800 everyday 3D object models spread across ~270 categories!
	Contrast and Classify: Training Robust VQA Models Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal ICCV, 2021 arXiv / project page / code / slides We propose a training scheme which steers VQA models towards answering paraphrased questions consistently, and we ended up beating previous baselines by an absolute 5.8% on consistency metrics without any performance drop!
	Spatially Aware Multimodal Transformers for TextVQA Yash Kant, Dhruv Batra, Peter Anderson, Jiasen Lu, Alexander Schwing, Devi Parikh, Harsh Agrawal ECCV, 2020 arXiv / project page / code / short talk / long talk / slides We built a self-attention module to reason over spatial graphs in images. We ended up with an absolute performance improvement of more than 4% on two TextVQA bechmarks!
	Automated Video Description for Blind and Low Vision Users Aditya Bodi, Pooyan Fazli, Shasta Ihorn, Yue-Ting Siu, Andrew T Scott, Lothar Narins, Yash Kant, Abhishek Das, Ilmi Yoon CHI Extended Abstracts, 2021 paper We built a system to automatically generate descriptions for videos and answer blind and low vision users’ queries on the videos!

Projects

Adding Complement Objective Training to Pythia: I experimented with adding Complement Objective Training in FAIR's vision and language framework Pythia and also wrote a report on my findings here, the code is here.
ICLR Reproducibility Challenge: We reproduced Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks and here's the code.
Visual Chatbot Version 2.0 (code here): I shifted the old Lua-Torch codebase to PyTorch, added better captioning and trained the VisDial model on BUTD features.
Quantized Neural Architecture Search: I quantized the search space of Neural Architecture Search algorithms (ENAS, PNAS) to search for resource-efficient models. Code is here (ENAS), and here (PNAS).

Fun

Inspired by @abhskdz's webpage, I got myself a conquer.earth account.

I borrowed this template from Jon Barron's website.