Search Results for author:
Found 5 papers, 0 papers with code
LEAP: LLM-Generation of Egocentric Action Programs
We apply LEAP over a majority (87\%) of the training set of the EPIC Kitchens dataset, and release the resulting action programs as a publicly available dataset here (https://drive. google. com/drive/folders/1Cpkw_TI1IIxXdzor0pOXG3rWJWuKU5Ex? usp=drive_link).
Therbligs in Action: Video Understanding through Motion Primitives
In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms.
Forecasting Action through Contact Representations from First Person Video
Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science.
Egocentric Object Manipulation Graphs
We introduce Egocentric Object Manipulation Graphs (Ego-OMG) - a novel representation for activity modeling and anticipation of near future actions integrating three components: 1) semantic temporal structure of activities, 2) short-term dynamics, and 3) representations for appearance.
Following Instructions by Imagining and Reaching Visual Goals
While traditional methods for instruction-following typically assume prior linguistic and perceptual knowledge, many recent works in reinforcement learning (RL) have proposed learning policies end-to-end, typically by training neural networks to map joint representations of observations and instructions directly to actions.
