Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?
© 2018 Copyright is held by the owner/author(s). In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM
2021
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| Online Access: | https://hdl.handle.net/1721.1/137932 |
| _version_ | 1826213984405028864 |
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| author | Kaspar, Alexandre Patterson, Genevieve Kim, Changil Aksoy, Yagiz Matusik, Wojciech Elgharib, Mohamed |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kaspar, Alexandre Patterson, Genevieve Kim, Changil Aksoy, Yagiz Matusik, Wojciech Elgharib, Mohamed |
| author_sort | Kaspar, Alexandre |
| collection | MIT |
| description | © 2018 Copyright is held by the owner/author(s). In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often complement each other so that they can be combined into something better than any of the individual results-The very spirit of collaborative working. For one, we extend a standard polygon-drawing interface to allow workers to annotate negative space, and combine the work of multiple workers instead of relying on a single best one as commonly done in crowdsourced image segmentation. For the other, we present a method to combine multiple automatic propagation algorithms with the help of the crowd. Such combination requires an understanding of where the algorithms fail, which we gather using a novel coarse scribble video annotation task. We evaluate our ensemble methods, discuss our design choices for them, and make our web-based crowdsourcing tools and results publicly available. |
| first_indexed | 2024-09-23T15:57:57Z |
| format | Article |
| id | mit-1721.1/137932 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:57:57Z |
| publishDate | 2021 |
| publisher | ACM |
| record_format | dspace |
| spelling | mit-1721.1/1379322023-01-18T20:27:48Z Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? How Can We Choreograph Crowd Workers for Video Segmentation? Kaspar, Alexandre Patterson, Genevieve Kim, Changil Aksoy, Yagiz Matusik, Wojciech Elgharib, Mohamed Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2018 Copyright is held by the owner/author(s). In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often complement each other so that they can be combined into something better than any of the individual results-The very spirit of collaborative working. For one, we extend a standard polygon-drawing interface to allow workers to annotate negative space, and combine the work of multiple workers instead of relying on a single best one as commonly done in crowdsourced image segmentation. For the other, we present a method to combine multiple automatic propagation algorithms with the help of the crowd. Such combination requires an understanding of where the algorithms fail, which we gather using a novel coarse scribble video annotation task. We evaluate our ensemble methods, discuss our design choices for them, and make our web-based crowdsourcing tools and results publicly available. 2021-11-09T15:47:21Z 2021-11-09T15:47:21Z 2018-04-19 2019-06-21T16:09:20Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137932 Kaspar, Alexandre, Patterson, Genevieve, Kim, Changil, Aksoy, Yagiz, Matusik, Wojciech et al. 2018. "Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?." en 10.1145/3173574.3173685 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM MIT web domain |
| spellingShingle | Kaspar, Alexandre Patterson, Genevieve Kim, Changil Aksoy, Yagiz Matusik, Wojciech Elgharib, Mohamed Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title | Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title_full | Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title_fullStr | Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title_full_unstemmed | Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title_short | Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation? |
| title_sort | crowd guided ensembles how can we choreograph crowd workers for video segmentation |
| url | https://hdl.handle.net/1721.1/137932 |
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