Update w-uncertainty-estimation-for-cv.md

sections/w-uncertainty-estimation-for-cv.md

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 | A Simple and Explainable Method for Uncertainty Estimation using Attribute Prototype Networks | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Zelenka_A_Simple_and_Explainable_Method_for_Uncertainty_Estimation_Using_Attribute_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
 | A Unified Approach to Learning with Label Noise and Unsupervised Confidence Approximation | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Rabbani_Unsupervised_Confidence_Approximation_Trustworthy_Learning_from_Noisy_Labelled_Data_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
 | Adversarial Attacks Against Uncertainty Quantification | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Ledda_Adversarial_Attacks_Against_Uncertainty_Quantification_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2309.10586-b31b1b.svg)](https://arxiv.org/abs/2309.10586) | :heavy_minus_sign: |
-| Biased Class Disagreement: Detection of Out of Distribution Instances by using Differently Biased Semantic Segmentation Models | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Alcover-Couso_Biased_Class_disagreement_detection_of_out_of_distribution_instances_by_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
+| Biased Class Disagreement: Detection of Out of Distribution Instances by using Differently Biased Semantic Segmentation Models | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Alcover-Couso_Biased_Class_disagreement_detection_of_out_of_distribution_instances_by_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
 | Calibrated Out-of-Distribution Detection with a Generic Representation | [![GitHub](https://img.shields.io/github/stars/vojirt/GROOD?style=flat)](https://github.com/vojirt/GROOD) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Vojir_Calibrated_Out-of-Distribution_Detection_with_a_Generic_Representation_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2303.13148-b31b1b.svg)](https://arxiv.org/abs/2303.13148) | :heavy_minus_sign: |
 | DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Ali_DELO_Deep_Evidential_LiDAR_Odometry_Using_Partial_Optimal_Transport_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2308.07153-b31b1b.svg)](https://arxiv.org/abs/2308.07153) | :heavy_minus_sign: |
 | Distance Matters for Improving Performance Estimation Under Covariate Shift | [![GitHub](https://img.shields.io/github/stars/melanibe/distance_matters_performance_estimation?style=flat)](https://github.com/melanibe/distance_matters_performance_estimation) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Roschewitz_Distance_Matters_For_Improving_Performance_Estimation_Under_Covariate_Shift_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2308.07223-b31b1b.svg)](https://arxiv.org/abs/2308.07223) | :heavy_minus_sign: |