The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Modeling Actions through State Changes
Abstract
In this paper we present a model of action based on the change in the state of the environment. Many actions involve similar dynamics and hand-object relationships, but differ in their purpose and meaning. The key to differentiating these actions is the ability to identify how they change the state of objects and materials in the environment. We propose a weakly supervised method for learning the object and material states that are necessary for recognizing daily actions. Once these state detectors are learned, we can apply them to input videos and pool their outputs to detect actions. We further demonstrate that our method can be used to segment discrete actions from a continuous video of an activity. Our results outperform state-of-the-art action recognition and activity segmentation results.
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“In the literature, our task goes by the name action segmentation [7, 8, 6, 15, 29, 16, 10] or action detection [28, 20, 21, 25].”
MethodMS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
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“Other approaches model actions based on the change in the state of objects and materials [18] or based on the interactions between hands and objects [19].”
BackgroundDelving into egocentric actions
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Metadata
- Authors
- Fathi, A., & Rehg, J. M.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2579-2586
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1967686239
- Cited by
- 145
- References
- 29
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 11 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 11 · 2019: 12 · 2018: 11 · 2017: 12 · 2016: 23 · 2015: 18 · 2014: 5
- FWCI
- 9.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation
- Multidisciplinary University Research Initiative
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- human action recognition, weakly supervised learning, activity segmentation, hand-object interaction, video action detection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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