Papers › Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms
Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms
Chun-Jung Lin, Sourav Garg, Tat-Jun Chin, Feras Dayoub
We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2, and integrates full-image cross-attention to address key challenges such as varying lighting, seasonal variations, and viewpoint differences. In order to effectively learn correspondences and mis-correspondences between an image pair for the change detection task, we propose to a) ``freeze'' the backbone in order to retain the generality of dense foundation features, and b) employ ``full-image'' cross-attention to better tackle the viewpoint variations between the image pair. We evaluate our approach on two benchmark datasets, VL-CMU-CD and PSCD, along with their viewpoint-varied versions. Our experiments demonstrate significant improvements in F1-score, particularly in scenarios involving geometric changes between image pairs. The results indicate our method's superior generalization capabilities over existing state-of-the-art approaches, showing robustness against photometric and geometric variations as well as better overall generalization when fine-tuned to adapt to new environments. Detailed ablation studies further validate the contributions of each component in our architecture. Source code will be made publicly available upon acceptance.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Change Detection | Unaligned-VL-CMU-CD (neighbor distance 2) | Robust-Scene-Change-Detection (Diff-View Augmentation) | F1-score | 0.784 | #1 of 2 | Archive leaderboard | report |
| Scene Change Detection | Unaligned-VL-CMU-CD (neighbor distance 2) | Robust-Scene-Change-Detection | F1-score | 0.739 | #2 of 2 | Archive leaderboard | report |
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