SMITE: Segment Me In TimE
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913697877721088 |
|---|---|
| author | Alimohammadi, Amirhossein Nag, Sauradip Taghanaki, Saeid Asgari Tagliasacchi, Andrea Hamarneh, Ghassan Amiri, Ali Mahdavi |
| author_facet | Alimohammadi, Amirhossein Nag, Sauradip Taghanaki, Saeid Asgari Tagliasacchi, Andrea Hamarneh, Ghassan Amiri, Ali Mahdavi |
| contents | Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In this paper, we address this issue by employing a pre-trained text to image diffusion model supplemented with an additional tracking mechanism. We demonstrate that our approach can effectively manage various segmentation scenarios and outperforms state-of-the-art alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SMITE: Segment Me In TimE Alimohammadi, Amirhossein Nag, Sauradip Taghanaki, Saeid Asgari Tagliasacchi, Andrea Hamarneh, Ghassan Amiri, Ali Mahdavi Computer Vision and Pattern Recognition Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In this paper, we address this issue by employing a pre-trained text to image diffusion model supplemented with an additional tracking mechanism. We demonstrate that our approach can effectively manage various segmentation scenarios and outperforms state-of-the-art alternatives. |
| title | SMITE: Segment Me In TimE |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.18538 |