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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In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1999.784637 · Cited by 6,925
Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X., Zoubin Ghahramani, A., & Lafferty, J. (2003) · Cited by 3,509
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481
Transductive Inference for Text Classification using Support Vector Machines
Joachims, T. (1999) · Cited by 2,698
Support Vector Machines for Multiple-Instance Learning
Andrews, S. J., Tsochantaridis, I., & Hofmann, T. (2002) · Cited by 1,404
Marszałek, M., Laptev, I., & Schmid, C. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206557 · Cited by 1,197
A Statistical Approach to Texture Classification from Single Images
Varma, M., & Zisserman, A. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4635-4 · Cited by 1,144
Discovering objects and their location in images
Šivic, J., Russell, B. C., Efros, A. A., Zisserman, A., & Freeman, W. T. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.77 · Cited by 973
A probabilistic framework for semi-supervised clustering
Basu, S., Bilenko, M., & Mooney, R. J. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014062 · Cited by 823
Flagged first, then by citations
Research works printed but not linked: 2
- spriggs, 2009, Temporal segmentation and activity classification from firstperson sensing, Egovision Workshop
- zhu, 2003, Semisupervised learning using gaussian fields and harmonic functions, ICML
Cited by
Sorted by citations
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953
“ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples from the 23 divers…”
BackgroundInfluentialTemporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238
“We use MERL Shopping which was designed for action detection, Georgia Tech Egocentric Activities which was designed for action segmentation, and 50 Salads which has been used for both.”
BackgroundMethodInfluentialA Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L. V., Groß, M. H., & Sorkine‐Hornung, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.85 · Cited by 2,039
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_7 · Cited by 1,059
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673
“We evaluate the proposed model on three challenging datasets: 50Salads [25], Georgia Tech Egocentric Activities (GTEA) [8], and the Breakfast dataset [12].”
Method
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
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