Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection

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.