The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Learning to recognize objects in egocentric activities
Open accessAbstract
This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know only the names of the objects which are present within it, and have no other knowledge regarding the appearance or location of objects. The key to our approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories. By using Multiple Instance Learning to match object instances across sequences, we discover and localize object occurrences. Object representations are refined through transduction and object-level classifiers are trained. We demonstrate encouraging results in detecting novel object instances using models produced by weakly-supervised learning.
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“The GTEA dataset contains 28 videos corresponding to 7 different activities, like preparing coffee or cheese sandwich, performed by 4 subjects.”
From MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation (2023) · cites this paper as [8]
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“GTEA is originally 15 fps (frames per second) videos, and 50 Salads is 30 fps videos.”
From Stacked encoder–decoder transformer with boundary smoothing for action segmentation (2022) · cites this paper as [8]
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“Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.”
From MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation (2023) · cites this paper as [8]
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“Our framework yields stateof-the-art results on two challenging datasets: 50 Salads [7] and GTEA [8].”
From Stacked encoder–decoder transformer with boundary smoothing for action segmentation (2022) · cites this paper as [8]
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Metadata
- Authors
- Fathi, A., Ren, X., & Rehg, J. M.
- Affiliations
- Georgia Institute of Technology (US)
- Intel (United States) (US)
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 3281-3288
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2031688197
- Cited by
- 557
- References
- 26
- Citations by year
- 2026: 23 · 2025: 39 · 2024: 47 · 2023: 41 · 2022: 43 · 2021: 57 · 2020: 63 · 2019: 44 · 2018: 36 · 2017: 32 · 2016: 57 · 2015: 31 · 2014: 21 · 2013: 12 · 2012: 10
- FWCI
- 10.47
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- 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 (0916687)
- Multidisciplinary University Research Initiative
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- egocentric video, housework, object recognition, weakly supervised learning, unsupervised segmentation, hand segmentation, object localization, multiple instance learning, transductive learning, novel object detection, activity recognition
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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