add equal contrib

index.Rmd

@@ -918,7 +918,7 @@ document.querySelector(".page-inner section > *:first-child").classList.add("two
 <div>
 <img src="images/paper_agg2exp.png">
 <a href="https://doi.org/10.48550/arXiv.2407.16653">Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models</a>
-<p>Maciej Chrabaszcz, Hubert Baniecki, Piotr Komorowski, Szymon Płotka, Przemysław Biecek</p>
+<p>Maciej Chrabaszcz<sup>=</sup>, Hubert Baniecki<sup>=</sup>, Piotr Komorowski, Szymon Płotka, Przemysław Biecek</p>
 <p><strong>WACV (2025)</strong></p>
 We introduce Agg^2Exp, a methodology for aggregating fine-grained voxel attributions of the segmentation model's predictions. Unlike classical explanation methods that primarily focus on the local feature attribution, Agg^2Exp enables a more comprehensive global view on the importance of predicted segments in 3D images. As a concrete use-case, we apply Agg^2Exp to discover knowledge acquired by the Swin UNEt TRansformer model trained on the TotalSegmentator v2 dataset for segmenting anatomical structures in computed tomography medical images.
 </div>
@@ -1008,7 +1008,7 @@ In this research manuscript, we explore the potential of ensembling explanations
 <div>
 <img src="images/redteaming_sam.png">
 <a href="https://openaccess.thecvf.com/content/CVPR2024W/AdvML/html/Jankowski_Red-Teaming_Segment_Anything_Model_CVPRW_2024_paper.html">Red-Teaming Segment Anything Model</a>
-<p> Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek</p>
+<p> Krzysztof Jankowski<sup>=</sup>, Bartlomiej Sobieski<sup>=</sup>, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek</p>
 <p><strong>CVPR Workshops (2024)</strong></p>
 The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis of SAM. We analyze the impact of style transfer on segmentation masks. We assess whether the model can be used for attacks on privacy, such as recognizing celebrities' faces. Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts.
 </div>