Safe Artificial Intelligence for All Domains
Coherent Concept-Based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis-
[pdf]
[bibtex]@InProceedings{Patricio_2023_CVPR, author = {Patr{\'\i}cio, Cristiano and Neves, Jo\~ao C. and Teixeira, Luis F.}, title = {Coherent Concept-Based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3799-3808} }
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Koenig_2023_CVPR, author = {Koenig, Alexander and Schambach, Maximilian and Otterbach, Johannes}, title = {Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3789-3798} }
A Novel Benchmark for Refinement of Noisy Localization Labels in Autolabeled Datasets for Object Detection-
[pdf]
[supp]
[bibtex]@InProceedings{Bar_2023_CVPR, author = {B\"ar, Andreas and Uhrig, Jonas and Umesh, Jeethesh Pai and Cordts, Marius and Fingscheidt, Tim}, title = {A Novel Benchmark for Refinement of Noisy Localization Labels in Autolabeled Datasets for Object Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3851-3860} }
Maximum Entropy Information Bottleneck for Uncertainty-Aware Stochastic Embedding-
[pdf]
[supp]
[bibtex]@InProceedings{An_2023_CVPR, author = {An, Sungtae and Jammalamadaka, Nataraj and Chong, Eunji}, title = {Maximum Entropy Information Bottleneck for Uncertainty-Aware Stochastic Embedding}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3809-3818} }
Interpretable Model-Agnostic Plausibility Verification for 2D Object Detectors Using Domain-Invariant Concept Bottleneck Models-
[pdf]
[bibtex]@InProceedings{Keser_2023_CVPR, author = {Keser, Mert and Schwalbe, Gesina and Nowzad, Azarm and Knoll, Alois}, title = {Interpretable Model-Agnostic Plausibility Verification for 2D Object Detectors Using Domain-Invariant Concept Bottleneck Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3891-3900} }
Revealing Hidden Context Bias in Segmentation and Object Detection Through Concept-Specific Explanations-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Dreyer_2023_CVPR, author = {Dreyer, Maximilian and Achtibat, Reduan and Wiegand, Thomas and Samek, Wojciech and Lapuschkin, Sebastian}, title = {Revealing Hidden Context Bias in Segmentation and Object Detection Through Concept-Specific Explanations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3829-3839} }
Optimizing Explanations by Network Canonization and Hyperparameter Search-
[pdf]
[supp]
[arXiv]
[bibtex]@InProceedings{Pahde_2023_CVPR, author = {Pahde, Frederik and Yolcu, Galip \"Umit and Binder, Alexander and Samek, Wojciech and Lapuschkin, Sebastian}, title = {Optimizing Explanations by Network Canonization and Hyperparameter Search}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3819-3828} }
Category Differences Matter: A Broad Analysis of Inter-Category Error in Semantic Segmentation-
[pdf]
[supp]
[bibtex]@InProceedings{Zhou_2023_CVPR, author = {Zhou, Jingxing and Beyerer, J\"urgen}, title = {Category Differences Matter: A Broad Analysis of Inter-Category Error in Semantic Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3870-3880} }
RL-CAM: Visual Explanations for Convolutional Networks Using Reinforcement Learning-
[pdf]
[bibtex]@InProceedings{Sarkar_2023_CVPR, author = {Sarkar, Soumyendu and Babu, Ashwin Ramesh and Mousavi, Sajad and Ghorbanpour, Sahand and Gundecha, Vineet and Guillen, Antonio and Luna, Ricardo and Naug, Avisek}, title = {RL-CAM: Visual Explanations for Convolutional Networks Using Reinforcement Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3861-3869} }
Beyond AUROC & Co. for Evaluating Out-of-Distribution Detection Performance-
[pdf]
[bibtex]@InProceedings{Humblot-Renaux_2023_CVPR, author = {Humblot-Renaux, Galadrielle and Escalera, Sergio and Moeslund, Thomas B.}, title = {Beyond AUROC \& Co. for Evaluating Out-of-Distribution Detection Performance}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3881-3890} }
Investigating CLIP Performance for Meta-Data Generation in AD Datasets-
[pdf]
[supp]
[bibtex]@InProceedings{Gannamaneni_2023_CVPR, author = {Gannamaneni, Sujan Sai and Sadaghiani, Arwin and Rao, Rohil Prakash and Mock, Michael and Akila, Maram}, title = {Investigating CLIP Performance for Meta-Data Generation in AD Datasets}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3840-3850} }