# Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection

arXiv: https://arxiv.org/abs/2408.10619 · HTML: https://mlsanity.com/paper/2408.10619/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)

## Abstract (arXiv)

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.