Improving Unsupervised Video Object Segmentation via Fake Flow Generation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909257545285632 |
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| author | Cho, Suhwan Lee, Minhyeok Lee, Jungho Kim, Donghyeong Lee, Seunghoon Woo, Sungmin Lee, Sangyoun |
| author_facet | Cho, Suhwan Lee, Minhyeok Lee, Jungho Kim, Donghyeong Lee, Seunghoon Woo, Sungmin Lee, Sangyoun |
| contents | Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-stream approaches that leverage both RGB images and optical flow maps have gained significant attention. However, the limited amount of training data remains a substantial challenge. In this study, we propose a novel data generation method that simulates fake optical flows from single images, thereby creating large-scale training data for stable network learning. Inspired by the observation that optical flow maps are highly dependent on depth maps, we generate fake optical flows by refining and augmenting the estimated depth maps of each image. By incorporating our simulated image-flow pairs, we achieve new state-of-the-art performance on all public benchmark datasets without relying on complex modules. We believe that our data generation method represents a potential breakthrough for future VOS research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Unsupervised Video Object Segmentation via Fake Flow Generation Cho, Suhwan Lee, Minhyeok Lee, Jungho Kim, Donghyeong Lee, Seunghoon Woo, Sungmin Lee, Sangyoun Computer Vision and Pattern Recognition Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-stream approaches that leverage both RGB images and optical flow maps have gained significant attention. However, the limited amount of training data remains a substantial challenge. In this study, we propose a novel data generation method that simulates fake optical flows from single images, thereby creating large-scale training data for stable network learning. Inspired by the observation that optical flow maps are highly dependent on depth maps, we generate fake optical flows by refining and augmenting the estimated depth maps of each image. By incorporating our simulated image-flow pairs, we achieve new state-of-the-art performance on all public benchmark datasets without relying on complex modules. We believe that our data generation method represents a potential breakthrough for future VOS research. |
| title | Improving Unsupervised Video Object Segmentation via Fake Flow Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.11714 |