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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.19210 |
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| _version_ | 1866911487050645504 |
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| author | Hudson, Finlay G. C. Smith, William A. P. |
| author_facet | Hudson, Finlay G. C. Smith, William A. P. |
| contents | We present Track Anything Behind Everything (TABE), a novel pipeline for zero-shot amodal video object segmentation. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexible, zero-shot inference. We pose amodal segmentation as generative outpainting from modal (visible) masks using a pretrained video diffusion model. We do not need to re-train the diffusion model to accommodate additional input channels but instead use a pretrained model that we fine-tune at test-time to allow specialisation towards the tracked object. Our TABE pipeline is specifically designed to handle amodal completion, even in scenarios where objects are completely occluded. Our model and code will all be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19210 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation Hudson, Finlay G. C. Smith, William A. P. Computer Vision and Pattern Recognition We present Track Anything Behind Everything (TABE), a novel pipeline for zero-shot amodal video object segmentation. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexible, zero-shot inference. We pose amodal segmentation as generative outpainting from modal (visible) masks using a pretrained video diffusion model. We do not need to re-train the diffusion model to accommodate additional input channels but instead use a pretrained model that we fine-tune at test-time to allow specialisation towards the tracked object. Our TABE pipeline is specifically designed to handle amodal completion, even in scenarios where objects are completely occluded. Our model and code will all be released. |
| title | Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.19210 |