diff --git a/README.md b/README.md
index 7abe79f..1773638 100644
--- a/README.md
+++ b/README.md
@@ -1310,7 +1310,7 @@ Contributions to improve the completeness of this list are greatly appreciated.
             <td>
                 <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-on-resource-efficient-dl-for-cv.md">Workshop on Resource Efficient Deep Learning for Computer Vision</a>
             </td>
-            <td colspan="4" rowspan="27" align="center"><i>Will soon be added</i></td>
+            <td colspan="4" rowspan="25" align="center"><i>Will soon be added</i></td>
         </tr>
         <tr>
             <td>
@@ -1436,11 +1436,24 @@ Contributions to improve the completeness of this list are greatly appreciated.
             <td>
                 <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-uncertainty-estimation-for-cv.md">Uncertainty Estimation for Computer Vision</a>
             </td>
+            <td>
+                <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-uncertainty-estimation-for-cv.md"><img src="https://img.shields.io/badge/14-42BA16" alt="Papers"></a>
+            </td>
+            <td>
+                <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-uncertainty-estimation-for-cv.md"><img src="https://img.shields.io/badge/9-b31b1b" alt="Preprints"></a>
+            </td>
+            <td>
+                <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-uncertainty-estimation-for-cv.md"><img src="https://img.shields.io/badge/8-1D7FBF" alt="Open Code"></a>
+            </td>
+            <td>
+                <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/w-uncertainty-estimation-for-cv.md"><img src="https://img.shields.io/badge/0-FF0000" alt="Videos"></a>
+            </td>
         </tr>
         <tr>
             <td>
                 <a href="https://github.com/DmitryRyumin/ICCV-2023-Papers/blob/main/sections/vision-and-language-algorithmic-reasoning-w.md">Vision-and-Language Algorithmic Reasoning Workshop</a>
             </td>
+            <td colspan="4" rowspan="1" align="center"><i>Will soon be added</i></td>
         </tr>
     </tbody>
 </table>
diff --git a/sections/w-uncertainty-estimation-for-cv.md b/sections/w-uncertainty-estimation-for-cv.md
index d4ac1bd..319fb4d 100644
--- a/sections/w-uncertainty-estimation-for-cv.md
+++ b/sections/w-uncertainty-estimation-for-cv.md
@@ -25,21 +25,21 @@
 
 ## Uncertainty Estimation for Computer Vision
 
-![Section Papers](https://img.shields.io/badge/Section%20Papers-soon-42BA16) ![Preprint Papers](https://img.shields.io/badge/Preprint%20Papers-soon-b31b1b) ![Papers with Open Code](https://img.shields.io/badge/Papers%20with%20Open%20Code-soon-1D7FBF) ![Papers with Video](https://img.shields.io/badge/Papers%20with%20Video-soon-FF0000)
+![Section Papers](https://img.shields.io/badge/Section%20Papers-14-42BA16) ![Preprint Papers](https://img.shields.io/badge/Preprint%20Papers-9-b31b1b) ![Papers with Open Code](https://img.shields.io/badge/Papers%20with%20Open%20Code-8-1D7FBF) ![Papers with Video](https://img.shields.io/badge/Papers%20with%20Video-0-FF0000)
 
 | **Title** | **Repo** | **Paper** | **Video** |
 |-----------|:--------:|:---------:|:---------:|
 | 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/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/SG2RL/papers/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: |
 | Dual-level Interaction for Domain Adaptive Semantic Segmentation | [![GitHub](https://img.shields.io/github/stars/RainJamesY/DIDA?style=flat)](https://github.com/RainJamesY/DIDA) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Yao_Dual-Level_Interaction_for_Domain_Adaptive_Semantic_Segmentation_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2307.07972-b31b1b.svg)](https://arxiv.org/abs/2307.07972) | :heavy_minus_sign: |
 | Exploring Inlier and Outlier Specification for Improved Medical OOD Detection | [![GitHub](https://img.shields.io/github/stars/LLNL/OODmedic?style=flat)](https://github.com/LLNL/OODmedic) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Narayanaswamy_Exploring_Inlier_and_Outlier_Specification_for_Improved_Medical_OOD_Detection_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
 | Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection | [![GitHub](https://img.shields.io/github/stars/silviogalesso/dense-ood-knns?style=flat)](https://github.com/silviogalesso/dense-ood-knns) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Galesso_Far_Away_in_the_Deep_Space_Dense_Nearest-Neighbor-Based_Out-of-Distribution_Detection_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2211.06660-b31b1b.svg)](https://arxiv.org/abs/2211.06660) | :heavy_minus_sign: |
-| Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers | | | |
-| Identifying Out-of-Domain Objects with Dirichlet Deep Neural Networks | | | |
-| Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression | | | |
-| Uncle-SLAM: Uncertainty Learning for Dense Neural SLAM | | | |
+| Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers | [![GitHub](https://img.shields.io/github/stars/vaishwarya96/MAPLE-uncertainty-estimation?style=flat)](https://github.com/vaishwarya96/MAPLE-uncertainty-estimation) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Venkataramanan_Gaussian_Latent_Representations_for_Uncertainty_Estimation_Using_Mahalanobis_Distance_in_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2305.13849-b31b1b.svg)](https://arxiv.org/abs/2305.13849) | :heavy_minus_sign: |
+| Identifying Out-of-Domain Objects with Dirichlet Deep Neural Networks | :heavy_minus_sign: | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Hammam_Identifying_Out-of-Domain_Objects_with_Dirichlet_Deep_Neural_Networks_ICCVW_2023_paper.pdf) | :heavy_minus_sign: |
+| Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression | [![GitHub](https://img.shields.io/github/stars/antonbaumann/MIMO-Unet?style=flat)](https://github.com/antonbaumann/MIMO-Unet) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Baumann_Probabilistic_MIMO_U-Net_Efficient_and_Accurate_Uncertainty_Estimation_for_Pixel-Wise_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2308.07477-b31b1b.svg)](https://arxiv.org/abs/2308.07477) | :heavy_minus_sign: |
+| Uncle-SLAM: Uncertainty Learning for Dense Neural SLAM | [![GitHub](https://img.shields.io/github/stars/kev-in-ta/UncLe-SLAM?style=flat)](https://github.com/kev-in-ta/UncLe-SLAM) | [![thecvf](https://img.shields.io/badge/pdf-thecvf-7395C5.svg)](https://openaccess.thecvf.com/content/ICCV2023W/UnCV/papers/Sandstrom_UncLe-SLAM_Uncertainty_Learning_for_Dense_Neural_SLAM_ICCVW_2023_paper.pdf) <br /> [![arXiv](https://img.shields.io/badge/arXiv-2306.11048-b31b1b.svg)](https://arxiv.org/abs/2306.11048) | :heavy_minus_sign: |