Towards Evaluating the Robustness of Visual State Space Models
<|reference_start|>Towards Evaluating the Robustness of Visual State Space Models: Vision State Space Models (VSSMs), a novel architecture that combines the strengths of recurrent neural networks and latent variable models, have demonstrated remarkable performance in visual perception tasks by efficiently capturing long-range dependencies and modeling complex visual dynamics. However, their robustness under natural and adversarial perturbations remains a critical concern. In this work, we present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios, including occlusions, image structure, common corruptions, and adversarial attacks, and compare their performance to well-established architectures such as transformers and Convolutional Neural Networks. Furthermore, we investigate the resilience of VSSMs to object-background compositional changes on sophisticated benchmarks designed to test model performance in complex visual scenes. We also assess their robustness on object detection and segmentation tasks using corrupted datasets that mimic real-world scenarios. To gain a deeper understanding of VSSMs' adversarial robustness, we conduct a frequency-based analysis of adversarial attacks, evaluating their performance against low-frequency and high-frequency perturbations. Our findings highlight the strengths and limitations of VSSMs in handling complex visual corruptions, offering valuable insights for future research. Our code and models will be available at https://github.com/HashmatShadab/MambaRobustness.<|reference_end|>
@article{malik2024towards,
    title={Towards Evaluating the Robustness of Visual State Space Models},
    author={Hashmat Shadab Malik, Fahad Shamshad, Muzammal Naseer, Karthik
  Nandakumar, Fahad Shahbaz Khan, Salman Khan},
    journal={arXiv preprint arXiv:2406.09407},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09407},
    primaryClass={cs.CV}
}