Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.10619 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909595092385792 |
|---|---|
| author | Kiruluta, Andrew Lundy, Eric Lemos, Andreas |
| author_facet | Kiruluta, Andrew Lundy, Eric Lemos, Andreas |
| contents | We present a unified change detection pipeline that combines instance level masking, multi\-scale attention within a denoising diffusion model, and per pixel semantic classification, all refined via SSIM to match human perception. By first isolating only temporally novel objects with Mask R\-CNN, then guiding diffusion updates through hierarchical cross attention to object and global contexts, and finally categorizing each pixel into one of C change types, our method delivers detailed, interpretable multi\-class maps. It outperforms traditional differencing, Siamese CNNs, and GAN\-based detectors by 10\-25 points in F1 and IoU on both synthetic and real world benchmarks, marking a new state of the art in remote sensing change detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection Kiruluta, Andrew Lundy, Eric Lemos, Andreas Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing We present a unified change detection pipeline that combines instance level masking, multi\-scale attention within a denoising diffusion model, and per pixel semantic classification, all refined via SSIM to match human perception. By first isolating only temporally novel objects with Mask R\-CNN, then guiding diffusion updates through hierarchical cross attention to object and global contexts, and finally categorizing each pixel into one of C change types, our method delivers detailed, interpretable multi\-class maps. It outperforms traditional differencing, Siamese CNNs, and GAN\-based detectors by 10\-25 points in F1 and IoU on both synthetic and real world benchmarks, marking a new state of the art in remote sensing change detection. |
| title | Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2408.10619 |