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Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jungbeom | - |
dc.contributor.author | Kim, Eunji | - |
dc.contributor.author | Lee, Sungmin | - |
dc.contributor.author | Lee, Jangho | - |
dc.contributor.author | Yoon, Sungroh | - |
dc.date.accessioned | 2022-10-26T07:23:42Z | - |
dc.date.available | 2022-10-26T07:23:42Z | - |
dc.date.created | 2022-10-19 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.citation | 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), pp.6807-6817 | - |
dc.identifier.issn | 1550-5499 | - |
dc.identifier.uri | https://hdl.handle.net/10371/186972 | - |
dc.description.abstract | When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICCV.2019.00691 | - |
dc.citation.journaltitle | 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) | - |
dc.identifier.wosid | 000548549201093 | - |
dc.identifier.scopusid | 2-s2.0-85081911329 | - |
dc.citation.endpage | 6817 | - |
dc.citation.startpage | 6807 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoon, Sungroh | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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