Research graph
References from Robustness of SAM: Segment Anything Under Corruptions and Beyond. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1007/s13042-024-02443-6
10.1007/s13042-024-02443-6 · External reference
10.18653/v1/n19-1423
10.18653/v1/n19-1423 · External reference
Learning transferable visual models from natural language supervision
External reference
Attack-SAM: Towards evaluating adversarial robustness of segment anything model
2023 · External reference
Language models are unsupervised multitask learners
2019 · External reference
10.1109/iccv51070.2023.00371
10.1109/iccv51070.2023.00371 · External reference
Segment anything with clip
2023 · External reference
Understanding segment anything model: SAM is biased towards texture rather than shape
2023 · External reference
Benchmarking neural network robustness to common corruptions and perturbations
External reference
Explaining and harnessing adversarial examples
External reference
10.48550/arxiv.1706.06083
10.48550/arxiv.1706.06083 · External reference
10.1109/cvpr52733.2024.00391
10.1109/cvpr52733.2024.00391 · External reference
10.1109/cvprw63382.2024.00185
10.1109/cvprw63382.2024.00185 · External reference
10.1609/aaai.v39i6.32616
10.1609/aaai.v39i6.32616 · External reference
10.1109/iccv.2017.167
10.1109/iccv.2017.167 · External reference
10.1109/tvcg.2012.160
10.1109/tvcg.2012.160 · External reference
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
External reference
NoisyMix: Boosting model robustness to common corruptions
External reference
10.1109/cvpr52729.2023.00105
10.1109/cvpr52729.2023.00105 · External reference
10.1016/j.media.2021.101977
10.1016/j.media.2021.101977 · External reference
10.5244/c.35.68
10.5244/c.35.68 · External reference
On success and simplicity: A second look at transferable targeted attacks
External reference
10.1109/tmm.2024.3521769
10.1109/tmm.2024.3521769 · External reference
10.1109/cvpr52733.2024.02293
10.1109/cvpr52733.2024.02293 · External reference
Representation learning with contrastive predictive coding
2018 · External reference
10.1007/978-3-319-46604-0_57
10.1007/978-3-319-46604-0_57 · External reference
10.1109/wacv48630.2021.00054
10.1109/wacv48630.2021.00054 · External reference
On the robustness of segment anything
2023 · External reference
10.1016/j.patcog.2024.110685
10.1016/j.patcog.2024.110685 · External reference
Faster segment anything: Towards lightweight sam for mobile applications
2023 · External reference
EdgeSAM: Prompt-in-the-loop distillation for on-device deployment of SAM
2023 · External reference
10.1609/aaai.v39i19.34255
10.1609/aaai.v39i19.34255 · External reference
10.1109/cvpr52688.2022.01839
10.1109/cvpr52688.2022.01839 · External reference
10.1109/iccv48922.2021.01420
10.1109/iccv48922.2021.01420 · External reference
Adversarial patch
2017 · External reference
Adam: A method for stochastic optimization
External reference
10.1109/tnnls.2023.3326871
10.1109/tnnls.2023.3326871 · External reference
10.1007/978-3-031-19775-8_24
10.1007/978-3-031-19775-8_24 · External reference
Insects semantic segmentation dataset
2024 · External reference
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
2019 · External reference
10.1007/978-3-319-10602-1_48
10.1007/978-3-319-10602-1_48 · External reference
Adversarial examples are not bugs, they are features
External reference
10.1109/cvpr.2018.00393
10.1109/cvpr.2018.00393 · External reference
mixup: Beyond empirical risk minimization
2017 · External reference
10.1109/cvpr.2019.00723
10.1109/cvpr.2019.00723 · External reference
10.1007/978-3-030-01264-9_28
10.1007/978-3-030-01264-9_28 · External reference
10.52202/075280-1425
10.52202/075280-1425 · External reference
RepViT-SAM: Towards real-time segmenting anything
2023 · External reference
A fourier perspective on model robustness in computer vision
External reference
Adversarial defense by suppressing high-frequency components
2019 · External reference
10.1109/tccn.2024.3360514
10.1109/tccn.2024.3360514 · External reference
A study of the effect of JPG compression on adversarial images
2016 · External reference
Mitigating adversarial effects through randomization
External reference
Detection as regression: Certified object detection with median smoothing
External reference
Trust, but verify: A survey of randomized smoothing techniques
2023 · External reference
On the robustness of the CVPR 2018 white-box adversarial example defenses
External reference
Decoupled weight decay regularization
External reference
10.1109/3dv.2016.79
10.1109/3dv.2016.79 · External reference
10.1007/s10479-005-5724-z
10.1007/s10479-005-5724-z · External reference
