no code implementations • 30 May 2024 • Himangi Mittal, Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee
To address this limitation, we explore the generative capability of a large video-language model in our work and further, develop the understanding of plausibility in an action sequence by introducing two objective functions, a counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss.
Ranked #1 on Action Anticipation on EPIC-KITCHENS-100
1 code implementation • 27 Sep 2022 • Himangi Mittal, Pedro Morgado, Unnat Jain, Abhinav Gupta
However, learning representations from videos can be challenging.
Ranked #3 on Object State Change Classification on Ego4D
1 code implementation • 21 Nov 2021 • Himangi Mittal, Brian Okorn, Arpit Jangid, David Held
The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using only partial observations.
no code implementations • 1 Dec 2019 • Himangi Mittal, Ajith Abraham, Anuja Arora
The context can be conducive to comprehending an image since it will help us to perceive the relation between the objects and thus, give us a deeper insight into the image.
1 code implementation • CVPR 2020 • Himangi Mittal, Brian Okorn, David Held
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects.
1 code implementation • 11 Nov 2017 • Supriya Pandhre, Himangi Mittal, Manish Gupta, Vineeth N. Balasubramanian
In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs.