38 citations · 92 across the 5 of their papers we have counts for
10 papers
LocATe: End-to-end Localization of Actions in 3D with Transformers
Jiankai Sun, Bolei Zhou, Michael J. Black +1
Understanding a person's behavior from their 3D motion is a fundamental problem in computer vision with many applications. An important component of this problem is 3D Temporal Act…
BABEL: Bodies, Action and Behavior with English Labels
Abhinanda R. Punnakkal, Arjun Chandrasekaran, Nikos Athanasiou +2
Understanding the semantics of human movement -- the what, how and why of the movement -- is an important problem that requires datasets of human actions with semantic labels. Exis…
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko +1
Generalizing deep neural networks to new target domains is critical to their real-world utility. In practice, it may be feasible to get some target data labeled, but to be cost-eff…
A Computational Model of Early Word Learning from the Infant's Point of View
Satoshi Tsutsui, Arjun Chandrasekaran, Md Alimoor Reza +2
Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences. Researchers in cognitive science…
Do Explanations make VQA Models more Predictable to a Human?
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav +2
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactiv…
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu +5
As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progr…