Several other visual foundation models could be explored to enhance the robustness and generalization of the scene change detection approach:
Vision Transformers (ViTs): Beyond DINOv2, other transformer-based models like ViT and Swin Transformer could be investigated. These models excel in capturing long-range dependencies and contextual information, which can be beneficial for understanding complex scene changes.
EfficientNet: This model family is known for its efficiency and performance in image classification tasks. By leveraging EfficientNet as a backbone, the scene change detection system could benefit from its ability to extract high-quality features while maintaining computational efficiency.
ResNeXt: This model introduces cardinality (the size of the set of transformations) as a new dimension in addition to depth and width, potentially leading to better feature extraction capabilities. Its architecture could be adapted for change detection tasks to improve robustness against variations.
Swin Transformer: This hierarchical transformer model processes images at different scales, which could be advantageous for handling changes that occur at various levels of detail. Its ability to capture both local and global features may enhance the model's performance in diverse scenarios.
Multi-Scale Feature Extractors: Models that incorporate multi-scale feature extraction, such as FPN (Feature Pyramid Networks), could be beneficial. They allow the model to capture features at different resolutions, which is crucial for detecting changes in scenes with varying scales and perspectives.
Generative Models: Exploring generative models like GANs (Generative Adversarial Networks) could also be valuable. They can be used to synthesize training data for rare change scenarios, improving the model's ability to generalize to unseen changes.
By experimenting with these alternative visual foundation models, the scene change detection approach can be further refined, leading to improved performance and adaptability in real-world applications.