Towards Evaluating the Robustness of Visual State Space Models
(2406.09407)Abstract
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 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 and improvements in this promising field. Our code and models will be available at https://github.com/HashmatShadab/MambaRobustness.
Overview
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The paper rigorously analyzes the robustness of Vision State Space Models (VSSMs) in handling different visual perturbations such as occlusions, data distribution shifts, common corruptions, and adversarial attacks, comparing their performance with Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).
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Experimental evaluations indicate that VSSMs exhibit superior performance across various conditions, particularly excelling in scenarios involving information drop, corruptions, and adversarial attacks on datasets like ImageNet-C, COCO, ADE20K, CIFAR-10, and Imagenette.
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The findings suggest that VSSMs hold promise for deployment in safety-critical applications, highlighting the importance of model architecture in achieving robustness and opening avenues for future research to further enhance their robustness and efficacy.
Evaluating the Robustness of Visual State Space Models
This paper presents a rigorous analysis of Vision State Space Models (VSSMs) and investigates their robustness across various perturbation scenarios, including occlusions, data distribution shifts, common corruptions, and adversarial attacks. By juxtaposing VSSMs against prevailing architectures such as Convolutional Neural Networks (CNNs) and Vision Transformers, the authors aim to highlight the strengths and weaknesses of VSSMs in handling visual perception tasks under challenging conditions.
Core Contributions
The researchers meticulously organized their experiments into several robustness evaluation categories, providing comprehensive insights into VSSMs:
- Robustness Against Information Drop: VSSMs adeptly manage information loss due to occlusions and random patch drops, outperforming ConvNext, ViT, and Swin models. However, under extreme conditions of information loss, Swin models prevail. VSSMs demonstrate superior robustness when image structures are disrupted, such as through patch shuffling or the selective removal of salient and non-salient patches.
- Resilience to Common Corruptions: Using ImageNet-C, the authors explore the performance degradation of VSSMs in response to 19 distinct corruption types at various intensities. Their findings showcase VSSMs' enhanced resilience to common corruptions compared to their Swin and ConvNext counterparts. On domain-shifted datasets, VSSM models consistently outperform their peers in achieving higher average accuracy across diverse conditions.
- Adversarial Robustness: Evaluating VSSMs' response to adversarial attacks, the paper details how VSSMs maintain higher robustness than Swin counterparts under the FGSM and PGD attacks in white-box settings. Furthermore, VSSMs exhibit superior performance against low-frequency perturbations while demonstrating robustness equivalent to that of other architectures under high-frequency perturbations.
Experimental Highlights
The authors conducted their analysis using a methodical approach, leveraging both classification and dense prediction tasks:
- Classification Tasks: The robustness of models was tested against occlusions, structural changes, and multiple common corruptions. VSSMs consistently demonstrated superior performance in maintaining top-1 classification accuracy as the severity of corruptions increased, outperforming ConvNext, ViT, and Swin transformer models. This robust performance extended to domain-shifted datasets, underscoring VSSMs' ability to generalize well across different visual data distributions.
- Detection and Segmentation Tasks: The study utilized the COCO and ADE20K datasets to assess detection and segmentation robustness. VSSMs showed superior resilience across common corruptions, maintaining higher accuracy scores in terms of mean Average Precision (mAP) and mean Intersection over Union (mIoU) compared to their transformer and convolutional counterparts.
- Adversarial Fine-Tuning: The adversarial fine-tuning results on CIFAR-10 and Imagenette datasets revealed VSSMs' strong performance on high-resolution images. While ViT models showed better results on lower-resolution datasets, VSSMs excelled in maintaining both clean and robust accuracy on high-resolution inputs.
Implications and Speculation
The comprehensive evaluation provided in this paper has several implications:
- Enhanced Robustness: VSSMs' robust performance across various perturbations suggests their potential suitability for deployment in safety-critical applications, such as autonomous vehicles and healthcare, where reliability under adverse conditions is paramount.
- Architectural Insights: The findings emphasize the importance of model architecture design in achieving robustness. VSSMs' ability to manage long-range dependencies and spatial interactions efficiently showcases their superiority in maintaining performance under corruptions and adversarial attacks.
- Future Directions: This work opens avenues for further research into refining VSSM architectures. Developing VSSMs with enhanced adversarial defenses and reduced vulnerability to high-frequency perturbations could yield models with even greater robustness and efficacy.
Conclusion
The paper's comprehensive robustness evaluation positions Vision State Space Models as a promising architecture in visual perception tasks, providing valuable insights for future research and practical applications. VSSMs exhibit superior performance in managing occlusions, data distribution shifts, common corruptions, and adversarial attacks while maintaining high computational efficiency. This study lays a foundational understanding of VSSMs' capabilities and limitations, propelling further advancements in robust visual perception systems.
This robust and meticulous evaluation of VSSMs against a backdrop of diverse and challenging visual scenarios underscores their potential and establishes a nuanced understanding of their applicability in complex, real-world environments.
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