Search Results for author: Tushar Nagarajan

Found 13 papers, 8 papers with code

Shaping embodied agent behavior with activity-context priors from egocentric video

no code implementations NeurIPS 2021 Tushar Nagarajan, Kristen Grauman

For a given object, an activity-context prior represents the set of other compatible objects that are required for activities to succeed (e. g., a knife and cutting board brought together with a tomato are conducive to cutting).

Ego4D: Around the World in 3,000 Hours of Egocentric Video

1 code implementation CVPR 2022 Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei HUANG, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.

De-identification

Environment Predictive Coding for Embodied Agents

no code implementations3 Feb 2021 Santhosh K. Ramakrishnan, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman

We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents.

Self-Supervised Learning

Differentiable Causal Discovery Under Unmeasured Confounding

1 code implementation14 Oct 2020 Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser

In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables.

Causal Discovery

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

1 code implementation NeurIPS 2020 Tushar Nagarajan, Kristen Grauman

We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen).

EGO-TOPO: Environment Affordances from Egocentric Video

1 code implementation CVPR 2020 Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, Kristen Grauman

We introduce a model for environment affordances that is learned directly from egocentric video.

Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)

no code implementations3 Jun 2019 Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman

Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements.

Human-Object Interaction Detection Semantic Segmentation

Grounded Human-Object Interaction Hotspots from Video

1 code implementation ICCV 2019 Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman

Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements.

Human-Object Interaction Detection Object Recognition +1

Attributes as Operators: Factorizing Unseen Attribute-Object Compositions

1 code implementation ECCV 2018 Tushar Nagarajan, Kristen Grauman

In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.

Compositional Zero-Shot Learning Image Retrieval with Multi-Modal Query

BlockDrop: Dynamic Inference Paths in Residual Networks

1 code implementation CVPR 2018 Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris

Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications.

CANDiS: Coupled & Attention-Driven Neural Distant Supervision

no code implementations26 Oct 2017 Tushar Nagarajan, Sharmistha, Partha Talukdar

The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data.

Relation Extraction

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