{"list":"other","total":110,"rows":[{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1606.06565","title":"Concrete Problems in AI Safety","created":"2016-06-21","cited_by_count":1642},{"id":"2102.04306","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","created":"2021-02-08","cited_by_count":1558},{"id":"1910.10045","title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","created":"2019-10-22","cited_by_count":1552},{"id":"1511.00561","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","created":"2015-11-02","cited_by_count":1487},{"id":"1907.07174","title":"Natural Adversarial Examples","created":"2019-07-16","cited_by_count":1408},{"id":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1706.07269","title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","created":"2017-06-22","cited_by_count":1206},{"id":"1810.03292","title":"Sanity Checks for Saliency Maps","created":"2018-10-08","cited_by_count":1156},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1711.00399","title":"Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR","created":"2017-11-01","cited_by_count":1089},{"id":"1608.05442","title":"Semantic Understanding of Scenes through the ADE20K Dataset","created":"2016-08-18","cited_by_count":1069},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"2105.05633","title":"Segmenter: Transformer for Semantic Segmentation","created":"2021-05-12","cited_by_count":855},{"id":"1806.07421","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","created":"2018-06-19","cited_by_count":838},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"1704.05796","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","created":"2017-04-19","cited_by_count":708},{"id":"1902.03368","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","created":"2019-02-09","cited_by_count":669},{"id":"1907.11922","title":"MaskGAN: Towards Diverse and Interactive Facial Image Manipulation","created":"2019-07-27","cited_by_count":628},{"id":"2106.05735","title":"The Medical Segmentation Decathlon","created":"2021-06-10","cited_by_count":628},{"id":"1911.07069","title":"Kvasir-SEG: A Segmented Polyp Dataset","created":"2019-11-16","cited_by_count":589},{"id":"1710.05006","title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","created":"2017-10-13","cited_by_count":549},{"id":"1901.04056","title":"The Liver Tumor Segmentation Benchmark (LiTS)","created":"2019-01-13","cited_by_count":509},{"id":"1902.09063","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","created":"2019-02-25","cited_by_count":373},{"id":"1711.00867","title":"The (Un)reliability of saliency methods","created":"2017-11-02","cited_by_count":333},{"id":"1902.07830","title":"Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges","created":"2019-02-21","cited_by_count":314},{"id":"1901.09392","title":"On the (In)fidelity and Sensitivity for Explanations","created":"2019-01-27","cited_by_count":310},{"id":"2001.06535","title":"CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation","created":"2020-01-17","cited_by_count":309},{"id":"2012.14261","title":"A Survey on Neural Network Interpretability","created":"2020-12-28","cited_by_count":291},{"id":"1703.08603","title":"Adversarial Examples for Semantic Segmentation and Object Detection","created":"2017-03-24","cited_by_count":275},{"id":"1812.04608","title":"Metrics for Explainable AI: Challenges and Prospects","created":"2018-12-11","cited_by_count":260},{"id":"1910.03667","title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","created":"2019-10-08","cited_by_count":228},{"id":"1908.02288","title":"BCN20000: Dermoscopic Lesions in the Wild","created":"2019-08-06","cited_by_count":225},{"id":"1612.00799","title":"A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images","created":"2016-12-02","cited_by_count":221},{"id":"1906.07983","title":"Explanations can be manipulated and geometry is to blame","created":"2019-06-19","cited_by_count":216},{"id":"2004.06320","title":"A2D2: Audi Autonomous Driving Dataset","created":"2020-04-14","cited_by_count":190},{"id":"1905.08883","title":"Explainable Machine Learning for Scientific Insights and Discoveries","created":"2019-05-21","cited_by_count":166},{"id":"1612.08820","title":"Multivariate mixture model for myocardium segmentation combining multi-source images","created":"2016-12-28","cited_by_count":133},{"id":"1910.07655","title":"Deep Semantic Segmentation of Natural and Medical Images: A Review","created":"2019-10-16","cited_by_count":125},{"id":"2111.15000","title":"Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes","created":"2021-11-29","cited_by_count":120},{"id":"1912.01451","title":"Sanity Checks for Saliency Metrics","created":"2019-11-29","cited_by_count":119},{"id":"1906.02825","title":"XRAI: Better Attributions Through Regions","created":"2019-06-06","cited_by_count":116},{"id":"1703.05446","title":"Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing","created":"2017-03-16","cited_by_count":109},{"id":"2112.03184","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","created":"2021-12-06","cited_by_count":105},{"id":"2104.14403","title":"Do Feature Attribution Methods Correctly Attribute Features?","created":"2021-04-27","cited_by_count":98},{"id":"2112.04417","title":"What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods","created":"2021-12-06","cited_by_count":98},{"id":"2112.02902","title":"Interpretable Image Classification with Differentiable Prototypes Assignment","created":"2021-12-06","cited_by_count":97},{"id":"2105.07190","title":"A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts","created":"2021-05-15","cited_by_count":91},{"id":"2001.02522","title":"On Interpretability of Artificial Neural Networks: A Survey","created":"2020-01-08","cited_by_count":88},{"id":"2003.09405","title":"Explainable Object-induced Action Decision for Autonomous Vehicles","created":"2020-03-20","cited_by_count":88},{"id":"2105.14944","title":"The effectiveness of feature attribution methods and its correlation with automatic evaluation scores","created":"2021-05-31","cited_by_count":88},{"id":"2009.10549","title":"CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation","created":"2020-09-22","cited_by_count":79},{"id":"2011.08891","title":"Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks","created":"2020-11-17","cited_by_count":79},{"id":"2212.11870","title":"Impossibility Theorems for Feature Attribution","created":"2022-12-22","cited_by_count":78},{"id":"2105.11266","title":"Argumentative XAI: A Survey","created":"2021-05-24","cited_by_count":67},{"id":"1912.02523","title":"Towards Explainable Deep Neural Networks (xDNN)","created":"2019-12-05","cited_by_count":61},{"id":"1912.05743","title":"Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning","created":"2019-12-09","cited_by_count":60},{"id":"2212.04629","title":"Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation","created":"2022-12-09","cited_by_count":59},{"id":"2011.08065","title":"Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy","created":"2020-10-23","cited_by_count":54},{"id":"1707.05373","title":"Houdini: Fooling Deep Structured Prediction Models","created":"2017-07-17","cited_by_count":50},{"id":"2002.11434","title":"Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping","created":"2020-02-26","cited_by_count":49},{"id":"2211.00541","title":"Logic-Based Explainability in Machine Learning","created":"2022-10-24","cited_by_count":48},{"id":"2303.01710","title":"BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability","created":"2023-03-03","cited_by_count":42},{"id":"2111.09094","title":"STEEX: Steering Counterfactual Explanations with Semantics","created":"2021-11-17","cited_by_count":40},{"id":"1703.01101","title":"Adversarial Examples for Semantic Image Segmentation","created":"2017-03-03","cited_by_count":33},{"id":"2301.12276","title":"ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts","created":"2023-01-28","cited_by_count":33},{"id":"1907.13054","title":"Grid Saliency for Context Explanations of Semantic Segmentation","created":"2019-07-30","cited_by_count":32},{"id":"2306.04719","title":"Don't trust your eyes: on the (un)reliability of feature visualizations","created":"2023-06-07","cited_by_count":32},{"id":"1702.05970","title":"Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks","created":"2017-02-20","cited_by_count":28},{"id":"2211.12380","title":"OCTET: Object-aware Counterfactual Explanations","created":"2022-11-22","cited_by_count":28},{"id":"1908.11757","title":"Temporal Coherence for Active Learning in Videos","created":"2019-08-30","cited_by_count":24},{"id":"2001.07645","title":"SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation","created":"2020-01-21","cited_by_count":19},{"id":"2211.11426","title":"Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations","created":"2022-11-21","cited_by_count":17},{"id":"2102.01792","title":"A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks","created":"2021-02-02","cited_by_count":14},{"id":"2102.04799","title":"Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images","created":"2021-02-09","cited_by_count":14},{"id":"2310.03715","title":"Artificial Intelligence Index Report 2023","created":"2023-10-05","cited_by_count":13},{"id":"2202.12295","title":"Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation","created":"2022-02-24","cited_by_count":12},{"id":"2101.08418","title":"Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection","created":"2021-01-21","cited_by_count":11},{"id":"2207.05358","title":"eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation","created":"2022-07-12","cited_by_count":11},{"id":"2007.09448","title":"Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability","created":"2020-07-18","cited_by_count":9},{"id":"2101.05791","title":"U-Noise: Learnable Noise Masks for Interpretable Image Segmentation","created":"2021-01-14","cited_by_count":9},{"id":"2212.09206","title":"Segmentation Ability Map: Interpret deep features for medical image segmentation","created":"2022-12-19","cited_by_count":9},{"id":"1912.02512","title":"Post-Mortem Iris Recognition Resistant to Biological Eye Decay Processes","created":"2019-12-05","cited_by_count":8},{"id":"2103.08590","title":"Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset","created":"2021-03-15","cited_by_count":8},{"id":"2112.00849","title":"Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition","created":"2021-12-01","cited_by_count":8},{"id":"2112.08645","title":"Learning Interpretable Models Through Multi-Objective Neural Architecture Search","created":"2021-12-16","cited_by_count":5},{"id":"2202.00315","title":"From Explanations to Segmentation: Using Explainable AI for Image Segmentation","created":"2022-02-01","cited_by_count":5},{"id":"2110.08429","title":"TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models","created":"2021-10-16","cited_by_count":4},{"id":"2210.12820","title":"An Interpretable Deep Semantic Segmentation Method for Earth Observation","created":"2022-10-23","cited_by_count":4},{"id":"2302.14762","title":"Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis","created":"2023-02-28","cited_by_count":4}],"next_cursor":"100"}