{"total":167,"rows":[{"id":"2106.03004","title":"Exploring the Limits of Out-of-Distribution Detection","created":"2021-06-06","cited_by_count":283,"area":null},{"id":"2201.09873","title":"Transformers in Medical Imaging: A Survey","created":"2022-01-24","cited_by_count":245,"area":null},{"id":"2105.07581","title":"Vision Transformers are Robust Learners","created":"2021-05-17","cited_by_count":207,"area":null},{"id":"2105.07926","title":"Towards Robust Vision Transformer","created":"2021-05-17","cited_by_count":137,"area":null},{"id":"2402.02309","title":"Jailbreaking Attack against Multimodal Large Language Model","created":"2024-02-04","cited_by_count":135,"area":"safety"},{"id":"2106.06047","title":"Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning","created":"2021-06-10","cited_by_count":111,"area":null},{"id":"2302.09462","title":"MedViT: A Robust Vision Transformer for Generalized Medical Image Classification","created":"2023-02-19","cited_by_count":89,"area":null},{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85,"area":null},{"id":"2109.05211","title":"RobustART: Benchmarking Robustness on Architecture Design and Training Techniques","created":"2021-09-11","cited_by_count":79,"area":null},{"id":"2106.04169","title":"On Improving Adversarial Transferability of Vision Transformers","created":"2021-06-08","cited_by_count":70,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2209.07399","title":"A Light Recipe to Train Robust Vision Transformers","created":"2022-09-15","cited_by_count":64,"area":null},{"id":"2210.07540","title":"When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture","created":"2022-10-14","cited_by_count":64,"area":null},{"id":"2111.04060","title":"Are we ready for a new paradigm shift? A Survey on Visual Deep MLP","created":"2021-11-07","cited_by_count":63,"area":null},{"id":"2201.05991","title":"Video Transformers: A Survey","created":"2022-01-16","cited_by_count":63,"area":null},{"id":"2110.02797","title":"Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs","created":"2021-10-06","cited_by_count":57,"area":null},{"id":"2203.08392","title":"Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?","created":"2022-03-16","cited_by_count":49,"area":null},{"id":"2111.14341","title":"OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images","created":"2021-11-29","cited_by_count":46,"area":null},{"id":"2106.03734","title":"Reveal of Vision Transformers Robustness against Adversarial Attacks","created":"2021-06-07","cited_by_count":43,"area":null},{"id":"2303.15754","title":"Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization","created":"2023-03-28","cited_by_count":42,"area":null},{"id":"2309.02031","title":"A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking","created":"2023-09-05","cited_by_count":41,"area":null},{"id":"2111.10659","title":"Are Vision Transformers Robust to Patch Perturbations?","created":"2021-11-20","cited_by_count":39,"area":null},{"id":"2111.15121","title":"Pyramid Adversarial Training Improves ViT Performance","created":"2021-11-30","cited_by_count":39,"area":null},{"id":"2312.04913","title":"SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation","created":"2023-12-08","cited_by_count":39,"area":"mllm"},{"id":"2403.14774","title":"Few-Shot Adversarial Prompt Learning on Vision-Language Models","created":"2024-03-21","cited_by_count":39,"area":"mllm"},{"id":"2204.08945","title":"Missingness Bias in Model Debugging","created":"2022-04-19","cited_by_count":38,"area":null},{"id":"2309.16108","title":"Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words","created":"2023-09-28","cited_by_count":34,"area":null},{"id":"2211.08025","title":"FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers","created":"2022-11-15","cited_by_count":33,"area":null},{"id":"2207.05739","title":"A Data-Based Perspective on Transfer Learning","created":"2022-07-12","cited_by_count":32,"area":null},{"id":"2307.07873","title":"Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability","created":"2023-07-15","cited_by_count":32,"area":null},{"id":"2306.07713","title":"Robustness of SAM: Segment Anything Under Corruptions and Beyond","created":"2023-06-13","cited_by_count":31,"area":null},{"id":"2308.07009","title":"ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion","created":"2023-08-14","cited_by_count":31,"area":null},{"id":"2207.10498","title":"Towards Efficient Adversarial Training on Vision Transformers","created":"2022-07-21","cited_by_count":30,"area":null},{"id":"2410.06699","title":"Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models","created":"2024-10-09","cited_by_count":27,"area":"mllm"},{"id":"2106.04823","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2203.13639","title":"Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness","created":"2022-03-25","cited_by_count":23,"area":null},{"id":"2206.07179","title":"Proximal Splitting Adversarial Attacks for Semantic Segmentation","created":"2022-06-14","cited_by_count":23,"area":null},{"id":"2209.14105","title":"Exploring the Relationship between Architecture and Adversarially Robust Generalization","created":"2022-09-28","cited_by_count":23,"area":null},{"id":"2403.18775","title":"ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object","created":"2024-03-27","cited_by_count":22,"area":"diffusion"},{"id":"2207.11971","title":"Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer","created":"2022-07-25","cited_by_count":21,"area":null},{"id":"2210.16765","title":"Benchmarking Adversarial Patch Against Aerial Detection","created":"2022-10-30","cited_by_count":21,"area":null},{"id":"2112.04016","title":"DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification","created":"2021-12-07","cited_by_count":20,"area":null},{"id":"2206.12381","title":"Defending Backdoor Attacks on Vision Transformer via Patch Processing","created":"2022-06-24","cited_by_count":20,"area":"safety"},{"id":"2303.11126","title":"Robustifying Token Attention for Vision Transformers","created":"2023-03-20","cited_by_count":19,"area":null},{"id":"2402.03951","title":"Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping","created":"2024-02-06","cited_by_count":18,"area":null},{"id":"2208.02851","title":"Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification","created":"2022-08-04","cited_by_count":16,"area":null},{"id":"2210.07448","title":"Evaluating Out-of-Distribution Performance on Document Image Classifiers","created":"2022-10-14","cited_by_count":16,"area":null},{"id":"2303.01542","title":"Self-attention in Vision Transformers Performs Perceptual Grouping, Not Attention","created":"2023-03-02","cited_by_count":16,"area":null},{"id":"2403.06925","title":"Transformers Learn Low Sensitivity Functions: Investigations and Implications","created":"2024-03-11","cited_by_count":14,"area":null},{"id":"2209.03358","title":"Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples","created":"2022-09-07","cited_by_count":13,"area":null},{"id":"2306.08565","title":"Reliable Evaluation of Adversarial Transferability","created":"2023-06-14","cited_by_count":13,"area":null},{"id":"2111.10481","title":"PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing","created":"2021-11-19","cited_by_count":12,"area":null},{"id":"2212.06872","title":"Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods","created":"2022-12-13","cited_by_count":12,"area":null},{"id":"2301.11553","title":"Robust Transformer with Locality Inductive Bias and Feature Normalization","created":"2023-01-27","cited_by_count":12,"area":null},{"id":"2304.10266","title":"OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images","created":"2023-04-17","cited_by_count":12,"area":null},{"id":"2309.13609","title":"Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks","created":"2023-09-24","cited_by_count":12,"area":null},{"id":"2311.17983","title":"Improving Interpretation Faithfulness for Vision Transformers","created":"2023-11-29","cited_by_count":12,"area":null},{"id":"2403.20254","title":"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions","created":"2024-03-29","cited_by_count":12,"area":null},{"id":"2110.04337","title":"Adversarial Token Attacks on Vision Transformers","created":"2021-10-08","cited_by_count":11,"area":null},{"id":"2210.05794","title":"Designing Robust Transformers using Robust Kernel Density Estimation","created":"2022-10-11","cited_by_count":11,"area":null},{"id":"2307.13856","title":"On the unreasonable vulnerability of transformers for image restoration -- and an easy fix","created":"2023-07-25","cited_by_count":11,"area":null},{"id":"2402.15989","title":"PIDformer: Transformer Meets Control Theory","created":"2024-02-25","cited_by_count":11,"area":null},{"id":"2407.19553","title":"Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection","created":"2024-07-28","cited_by_count":11,"area":null},{"id":"2408.13809","title":"On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective","created":"2024-08-25","cited_by_count":11,"area":null},{"id":"2305.19402","title":"Contextual Vision Transformers for Robust Representation Learning","created":"2023-05-30","cited_by_count":10,"area":null},{"id":"2307.02092","title":"Make A Long Image Short: Adaptive Token Length for Vision Transformers","created":"2023-07-05","cited_by_count":10,"area":null},{"id":"2308.12097","title":"Instruction Position Matters in Sequence Generation with Large Language Models","created":"2023-08-23","cited_by_count":10,"area":null},{"id":"2311.18495","title":"Improving Adversarial Transferability via Model Alignment","created":"2023-11-30","cited_by_count":10,"area":"alignment"},{"id":"2401.09109","title":"Trapped in texture bias? A large scale comparison of deep instance segmentation","created":"2024-01-17","cited_by_count":10,"area":null},{"id":"2406.04419","title":"TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification","created":"2024-06-06","cited_by_count":10,"area":"ssm"},{"id":"2111.11870","title":"DBIA: Data-free Backdoor Injection Attack against Transformer Networks","created":"2021-11-22","cited_by_count":9,"area":"safety"},{"id":"2208.09602","title":"Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective","created":"2022-08-20","cited_by_count":8,"area":null},{"id":"2301.12993","title":"Benchmarking Robustness to Adversarial Image Obfuscations","created":"2023-01-30","cited_by_count":8,"area":null},{"id":"2407.08972","title":"Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness","created":"2024-07-12","cited_by_count":8,"area":null},{"id":"2302.13519","title":"CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World","created":"2023-02-27","cited_by_count":7,"area":null},{"id":"2402.13575","title":"Flexible Physical Camouflage Generation Based on a Differential Approach","created":"2024-02-21","cited_by_count":7,"area":null},{"id":"2406.13770","title":"Elliptical Attention","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2206.04242","title":"OOD Augmentation May Be at Odds with Open-Set Recognition","created":"2022-06-09","cited_by_count":6,"area":null},{"id":"2211.12624","title":"Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2211.14669","title":"Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning","created":"2022-11-26","cited_by_count":6,"area":null},{"id":"2302.10287","title":"CertViT: Certified Robustness of Pre-Trained Vision Transformers","created":"2023-02-01","cited_by_count":6,"area":null},{"id":"2204.12143","title":"Deeper Insights into the Robustness of ViTs towards Common Corruptions","created":"2022-04-26","cited_by_count":5,"area":null},{"id":"2305.00866","title":"Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples","created":"2023-05-01","cited_by_count":5,"area":null},{"id":"2306.12111","title":"A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking","created":"2023-06-21","cited_by_count":5,"area":null},{"id":"2602.03284","title":"Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks","created":"2026-02-03","cited_by_count":5,"area":null},{"id":"2206.06761","title":"Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2208.00906","title":"Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem","created":"2022-08-01","cited_by_count":4,"area":null},{"id":"2209.02997","title":"On the Transferability of Adversarial Examples between Encrypted Models","created":"2022-09-07","cited_by_count":4,"area":null},{"id":"2209.13113","title":"FG-UAP: Feature-Gathering Universal Adversarial Perturbation","created":"2022-09-27","cited_by_count":4,"area":null},{"id":"2212.13764","title":"Representation Separation for Semantic Segmentation with Vision Transformers","created":"2022-12-28","cited_by_count":4,"area":null},{"id":"2304.04175","title":"Token Boosting for Robust Self-Supervised Visual Transformer Pre-training","created":"2023-04-09","cited_by_count":4,"area":null},{"id":"2207.02349","title":"Generalization to translation shifts: a study in architectures and augmentations","created":"2022-07-05","cited_by_count":3,"area":null},{"id":"2303.00284","title":"To Make Yourself Invisible with Adversarial Semantic Contours","created":"2023-03-01","cited_by_count":3,"area":null},{"id":"2308.08160","title":"Benchmarking Adversarial Robustness of Compressed Deep Learning Models","created":"2023-08-16","cited_by_count":3,"area":null},{"id":"2309.13245","title":"RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias","created":"2023-09-23","cited_by_count":3,"area":null},{"id":"2402.07183","title":"A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense","created":"2024-02-11","cited_by_count":3,"area":null},{"id":"2402.15586","title":"Distilling Adversarial Robustness Using Heterogeneous Teachers","created":"2024-02-23","cited_by_count":3,"area":"distill"},{"id":"2403.07942","title":"Attacking Transformers with Feature Diversity Adversarial Perturbation","created":"2024-03-10","cited_by_count":3,"area":null},{"id":"2406.01765","title":"Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2407.03946","title":"TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers","created":"2024-07-04","cited_by_count":3,"area":null}],"next_cursor":"100"}