no code implementations • 21 Mar 2022 • Jiankai Sun, Bolei Zhou, Michael J. Black, Arjun Chandrasekaran
An important component of this problem is 3D Temporal Action Localization (3D-TAL), which involves recognizing what actions a person is performing, and when.
1 code implementation • EMNLP 2021 • Ashwin Kalyan, Abhinav Kumar, Arjun Chandrasekaran, Ashish Sabharwal, Peter Clark
FPs are commonly used in quizzes and interviews to bring out and evaluate the creative reasoning abilities of humans.
1 code implementation • CVPR 2021 • Abhinanda R. Punnakkal, Arjun Chandrasekaran, Nikos Athanasiou, Alejandra Quiros-Ramirez, Michael J. Black
To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences.
Ranked #1 on Action Classification on BABEL
1 code implementation • ICCV 2021 • Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, Judy Hoffman
Generalizing deep neural networks to new target domains is critical to their real-world utility.
1 code implementation • 4 Jun 2020 • Satoshi Tsutsui, Arjun Chandrasekaran, Md. Alimoor Reza, David Crandall, Chen Yu
Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences.
no code implementations • EMNLP 2018 • Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA).
no code implementations • 17 Aug 2017 • Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.
1 code implementation • NAACL 2018 • Arjun Chandrasekaran, Devi Parikh, Mohit Bansal
Wit is a form of rich interaction that is often grounded in a specific situation (e. g., a comment in response to an event).
no code implementations • 3 Apr 2017 • Arjun Chandrasekaran, Deshraj Yadav, Prithvijit Chattopadhyay, Viraj Prabhu, Devi Parikh
Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.
no code implementations • EMNLP 2016 • Harsh Agrawal, Arjun Chandrasekaran, Dhruv Batra, Devi Parikh, Mohit Bansal
Temporal common sense has applications in AI tasks such as QA, multi-document summarization, and human-AI communication.
no code implementations • CVPR 2016 • Arjun Chandrasekaran, Ashwin K. Vijayakumar, Stanislaw Antol, Mohit Bansal, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh
We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level.