{"total":247,"rows":[{"id":"2106.08265","title":"Towards Total Recall in Industrial Anomaly Detection","created":"2021-06-15","cited_by_count":776,"area":null},{"id":"2103.00112","title":"Transformer in Transformer","created":"2021-02-27","cited_by_count":633,"area":null},{"id":"2105.10497","title":"Intriguing Properties of Vision Transformers","created":"2021-05-21","cited_by_count":446,"area":null},{"id":"2204.02937","title":"Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations","created":"2022-04-06","cited_by_count":381,"area":null},{"id":"2206.02647","title":"Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning","created":"2022-06-06","cited_by_count":359,"area":null},{"id":"2104.08894","title":"The Intrinsic Dimension of Images and Its Impact on Learning","created":"2021-04-18","cited_by_count":315,"area":null},{"id":"2104.09937","title":"Gradient Matching for Domain Generalization","created":"2021-04-20","cited_by_count":258,"area":null},{"id":"2106.07411","title":"Partial success in closing the gap between human and machine vision","created":"2021-06-14","cited_by_count":185,"area":null},{"id":"2203.06717","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","created":"2022-03-13","cited_by_count":155,"area":null},{"id":"2210.11369","title":"On Feature Learning in the Presence of Spurious Correlations","created":"2022-10-20","cited_by_count":145,"area":null},{"id":"2107.01372","title":"Learning Debiased Representation via Disentangled Feature Augmentation","created":"2021-07-03","cited_by_count":116,"area":null},{"id":"2105.07197","title":"Are Convolutional Neural Networks or Transformers more like human vision?","created":"2021-05-15","cited_by_count":109,"area":null},{"id":"2112.03184","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","created":"2021-12-06","cited_by_count":105,"area":null},{"id":"2112.02902","title":"Interpretable Image Classification with Differentiable Prototypes Assignment","created":"2021-12-06","cited_by_count":97,"area":null},{"id":"2106.05786","title":"CAT: Cross Attention in Vision Transformer","created":"2021-06-10","cited_by_count":92,"area":null},{"id":"2205.10268","title":"B-cos Networks: Alignment is All We Need for Interpretability","created":"2022-05-20","cited_by_count":88,"area":null},{"id":"2108.08782","title":"Causal Attention for Unbiased Visual Recognition","created":"2021-08-19","cited_by_count":80,"area":null},{"id":"2204.00993","title":"Improving Vision Transformers by Revisiting High-frequency Components","created":"2022-04-03","cited_by_count":76,"area":null},{"id":"2106.02890","title":"Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?","created":"2021-06-05","cited_by_count":75,"area":null},{"id":"2108.09666","title":"Relational Embedding for Few-Shot Classification","created":"2021-08-22","cited_by_count":74,"area":null},{"id":"2104.03149","title":"Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering","created":"2021-04-07","cited_by_count":71,"area":null},{"id":"2306.11074","title":"Simple and Fast Group Robustness by Automatic Feature Reweighting","created":"2023-06-19","cited_by_count":68,"area":null},{"id":"2210.00055","title":"MaskTune: Mitigating Spurious Correlations by Forcing to Explore","created":"2022-09-30","cited_by_count":66,"area":null},{"id":"2105.02968","title":"This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks","created":"2021-05-05","cited_by_count":65,"area":null},{"id":"2210.03735","title":"\"Help Me Help the AI\": Understanding How Explainability Can Support Human-AI Interaction","created":"2022-10-02","cited_by_count":63,"area":null},{"id":"2211.04533","title":"Harmonizing the object recognition strategies of deep neural networks with humans","created":"2022-11-08","cited_by_count":59,"area":null},{"id":"2309.00035","title":"FACET: Fairness in Computer Vision Evaluation Benchmark","created":"2023-08-31","cited_by_count":58,"area":null},{"id":"2104.00032","title":"Convolutional Dynamic Alignment Networks for Interpretable Classifications","created":"2021-03-31","cited_by_count":54,"area":null},{"id":"2212.02291","title":"I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification","created":"2022-12-05","cited_by_count":54,"area":null},{"id":"2101.11604","title":"Shape or Texture: Understanding Discriminative Features in CNNs","created":"2021-01-27","cited_by_count":52,"area":null},{"id":"2206.10843","title":"Learning Debiased Classifier with Biased Committee","created":"2022-06-22","cited_by_count":51,"area":null},{"id":"2406.09398","title":"Real-Time Deepfake Detection in the Real-World","created":"2024-06-13","cited_by_count":51,"area":null},{"id":"2103.01396","title":"DeepReDuce: ReLU Reduction for Fast Private Inference","created":"2021-03-02","cited_by_count":50,"area":null},{"id":"2308.06248","title":"FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods","created":"2023-08-11","cited_by_count":50,"area":null},{"id":"2207.09615","title":"Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability","created":"2022-07-20","cited_by_count":46,"area":null},{"id":"2203.08519","title":"Towards Practical Certifiable Patch Defense with Vision Transformer","created":"2022-03-16","cited_by_count":44,"area":null},{"id":"2110.14189","title":"Robust Contrastive Learning Using Negative Samples with Diminished Semantics","created":"2021-10-27","cited_by_count":43,"area":null},{"id":"2110.07858","title":"Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation","created":"2021-10-15","cited_by_count":40,"area":null},{"id":"2111.02625","title":"Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples","created":"2021-11-04","cited_by_count":40,"area":null},{"id":"2211.05568","title":"Unbiased Supervised Contrastive Learning","created":"2022-11-10","cited_by_count":40,"area":null},{"id":"2103.10292","title":"How I failed machine learning in medical imaging -- shortcomings and recommendations","created":"2021-03-18","cited_by_count":37,"area":null},{"id":"2111.09999","title":"TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems","created":"2021-11-19","cited_by_count":36,"area":null},{"id":"2110.05922","title":"Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)","created":"2021-10-12","cited_by_count":35,"area":null},{"id":"2109.01663","title":"Global-Local Transformer for Brain Age Estimation","created":"2021-09-03","cited_by_count":34,"area":null},{"id":"2203.15064","title":"Cycle-Consistent Counterfactuals by Latent Transformations","created":"2022-03-28","cited_by_count":33,"area":null},{"id":"2301.12276","title":"ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts","created":"2023-01-28","cited_by_count":33,"area":null},{"id":"2406.09415","title":"An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels","created":"2024-06-13","cited_by_count":33,"area":null},{"id":"2104.09789","title":"Does enhanced shape bias improve neural network robustness to common corruptions?","created":"2021-04-20","cited_by_count":32,"area":null},{"id":"2112.11407","title":"Toward Explainable AI for Regression Models","created":"2021-12-21","cited_by_count":31,"area":null},{"id":"2306.10898","title":"B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers","created":"2023-06-19","cited_by_count":31,"area":null},{"id":"2205.07972","title":"Sparse Visual Counterfactual Explanations in Image Space","created":"2022-05-16","cited_by_count":30,"area":null},{"id":"2102.02956","title":"DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks","created":"2021-02-05","cited_by_count":29,"area":null},{"id":"2104.12609","title":"PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches","created":"2021-04-26","cited_by_count":29,"area":null},{"id":"2107.13335","title":"WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification","created":"2021-07-28","cited_by_count":29,"area":null},{"id":"2211.02291","title":"SelecMix: Debiased Learning by Contradicting-pair Sampling","created":"2022-11-04","cited_by_count":29,"area":null},{"id":"2301.13293","title":"Overcoming Simplicity Bias in Deep Networks using a Feature Sieve","created":"2023-01-30","cited_by_count":29,"area":null},{"id":"2107.14344","title":"Towards robust vision by multi-task learning on monkey visual cortex","created":"2021-07-29","cited_by_count":28,"area":null},{"id":"2108.09301","title":"Discriminative Region-based Multi-Label Zero-Shot Learning","created":"2021-08-20","cited_by_count":27,"area":null},{"id":"2203.10444","title":"VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning","created":"2022-03-20","cited_by_count":27,"area":null},{"id":"2308.08162","title":"Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2101.12322","title":"Position, Padding and Predictions: A Deeper Look at Position Information in CNNs","created":"2021-01-28","cited_by_count":26,"area":null},{"id":"2102.04154","title":"Efficient Certified Defenses Against Patch Attacks on Image Classifiers","created":"2021-02-08","cited_by_count":24,"area":null},{"id":"2111.11802","title":"Pruning Self-attentions into Convolutional Layers in Single Path","created":"2021-11-23","cited_by_count":24,"area":null},{"id":"2205.11083","title":"Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation","created":"2022-05-23","cited_by_count":24,"area":null},{"id":"2206.08871","title":"How Robust is Unsupervised Representation Learning to Distribution Shift?","created":"2022-06-17","cited_by_count":24,"area":null},{"id":"2303.07811","title":"ICICLE: Interpretable Class Incremental Continual Learning","created":"2023-03-14","cited_by_count":24,"area":null},{"id":"2104.02215","title":"When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes","created":"2021-04-06","cited_by_count":23,"area":null},{"id":"2201.12078","title":"You Only Cut Once: Boosting Data Augmentation with a Single Cut","created":"2022-01-28","cited_by_count":22,"area":null},{"id":"2205.15704","title":"Mitigating Dataset Bias by Using Per-sample Gradient","created":"2022-05-31","cited_by_count":22,"area":null},{"id":"2401.00148","title":"TPatch: A Triggered Physical Adversarial Patch","created":"2023-12-30","cited_by_count":22,"area":null},{"id":"2109.03351","title":"Capturing the objects of vision with neural networks","created":"2021-09-07","cited_by_count":21,"area":null},{"id":"2211.14058","title":"Cross-Domain Ensemble Distillation for Domain Generalization","created":"2022-11-25","cited_by_count":21,"area":null},{"id":"2306.03301","title":"Estimating Conditional Mutual Information for Dynamic Feature Selection","created":"2023-06-05","cited_by_count":21,"area":null},{"id":"2205.14594","title":"Revisiting the Importance of Amplifying Bias for Debiasing","created":"2022-05-29","cited_by_count":19,"area":null},{"id":"2304.03977","title":"EMP-SSL: Towards Self-Supervised Learning in One Training Epoch","created":"2023-04-08","cited_by_count":19,"area":null},{"id":"2102.10212","title":"Hard-Attention for Scalable Image Classification","created":"2021-02-20","cited_by_count":18,"area":null},{"id":"2209.03416","title":"Bispectral Neural Networks","created":"2022-09-07","cited_by_count":18,"area":null},{"id":"2303.00865","title":"AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images","created":"2023-03-01","cited_by_count":18,"area":null},{"id":"2304.14252","title":"FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations","created":"2023-04-27","cited_by_count":18,"area":null},{"id":"2306.03779","title":"Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex","created":"2023-06-06","cited_by_count":18,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2106.02694","title":"Efficient Classification of Very Large Images with Tiny Objects","created":"2021-06-04","cited_by_count":17,"area":null},{"id":"2206.08304","title":"Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey","created":"2022-06-16","cited_by_count":17,"area":null},{"id":"2111.04104","title":"Uncertainty Calibration for Ensemble-Based Debiasing Methods","created":"2021-11-07","cited_by_count":16,"area":null},{"id":"2204.11433","title":"Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning","created":"2022-04-25","cited_by_count":16,"area":null},{"id":"2207.09510","title":"Contributions of Shape, Texture, and Color in Visual Recognition","created":"2022-07-19","cited_by_count":16,"area":null},{"id":"2209.05980","title":"Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation","created":"2022-09-13","cited_by_count":16,"area":null},{"id":"2307.12344","title":"Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?","created":"2023-07-23","cited_by_count":16,"area":null},{"id":"2110.14120","title":"ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers","created":"2021-10-27","cited_by_count":15,"area":null},{"id":"2205.01657","title":"Cross-modal Representation Learning for Zero-shot Action Recognition","created":"2022-05-03","cited_by_count":15,"area":null},{"id":"2307.14517","title":"The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers","created":"2023-07-26","cited_by_count":15,"area":null},{"id":"2405.14331","title":"LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision","created":"2024-05-23","cited_by_count":15,"area":null},{"id":"2204.11642","title":"Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset","created":"2022-04-25","cited_by_count":13,"area":null},{"id":"2509.20234","title":"ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression","created":"2025-09-24","cited_by_count":13,"area":null},{"id":"2110.08220","title":"Combining Diverse Feature Priors","created":"2021-10-15","cited_by_count":12,"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":"2211.04793","title":"RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection","created":"2022-11-09","cited_by_count":12,"area":null},{"id":"2112.01955","title":"Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion","created":"2021-12-03","cited_by_count":11,"area":null},{"id":"2211.01671","title":"Visual Adversarial Attacks and Defenses in the Physical World: A Survey","created":"2022-11-03","cited_by_count":11,"area":null},{"id":"2310.13076","title":"PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses","created":"2023-10-19","cited_by_count":11,"area":null}],"next_cursor":"100"}