The content discusses the robustness of deep learning models, specifically Vision State-Space Models (VSSMs), in handling various natural and adversarial disturbances compared to Convolutional Neural Networks (CNNs) and Vision Transformers. Researchers evaluated the performance of VSSMs in dealing with occlusions, common corruptions, and adversarial attacks, highlighting their strengths and weaknesses. The study revealed that VSSMs outperformed transformers and CNNs in certain scenarios, such as handling information loss and global corruptions. The experiments showed that VSSMs have the potential to adapt to changes in object-background composition in complex visual scenes. The research provides valuable insights into the reliability and effectiveness of visual perception systems in real-world applications. The findings suggest that VSSMs exhibit robustness against adversarial attacks and common corruptions, making them suitable for security-critical applications. The study also emphasized the importance of evaluating model performance under tough conditions to ensure their reliability. Researchers from MBZUAI UAE, Linkoping University, and ANU Australia conducted the comprehensive analysis, which can guide future research in enhancing visual perception systems. The paper and code repository for this research are available for further exploration.
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Source link: https://www.marktechpost.com/2024/06/30/comprehensive-analysis-of-the-performance-of-vision-state-space-models-vssms-vision-transformers-and-convolutional-neural-networks-cnns/?amp
Comparing Vision State Space Models, Vision Transformers, and CNNs #ComputerVision
