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<h1>Accepted Papers<a class="headerlink" href="#accepted-papers" title="Permalink to this heading">#</a></h1>
<section id="track-1-benchmarks-datasets-and-metrics">
<h2>Track 1: Benchmarks, Datasets and Metrics<a class="headerlink" href="#track-1-benchmarks-datasets-and-metrics" title="Permalink to this heading">#</a></h2>
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<h3>Contributed Talks<a class="headerlink" href="#contributed-talks" title="Permalink to this heading">#</a></h3>
<dl class="simple">
<dt>ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches.</dt><dd><p>Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli.</p>
</dd>
<dt>MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts.</dt><dd><p>Weixin Liang, Xinyu Yang, James Y. Zou.</p>
</dd>
<dt>When does dough become a bagel?Analyzing the remaining mistakes on ImageNet.</dt><dd><p>Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs.</p>
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<h3>Posters<a class="headerlink" href="#posters" title="Permalink to this heading">#</a></h3>
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<dt>3D Common Corruptions for Object Recognition.</dt><dd><p>Oguzhan Fatih Kar, Teresa Yeo, Amir Zamir.</p>
</dd>
<dt>CCC: Continuously Changing Corruptions.</dt><dd><p>Ori Press, Steffen Schneider, Matthias Kuemmerer, Matthias Bethge.</p>
</dd>
<dt>Classifiers Should Do Well Even on Their Worst Classes.</dt><dd><p>Julian Bitterwolf, Alexander Meinke, Valentyn Boreiko, Matthias Hein.</p>
</dd>
<dt>Evaluating Model Robustness to Patch Perturbations.</dt><dd><p>Jindong Gu, Volker Tresp, Yao Qin.</p>
</dd>
<dt>ImageNet-Cartoon and ImageNet-Drawing: two domain shift datasets for ImageNet.</dt><dd><p>Tiago Salvador, Adam M Oberman.</p>
</dd>
<dt>ImageNet-D: A new challenging robustness dataset inspired by domain adaptation.</dt><dd><p>Evgenia Rusak, Steffen Schneider, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge.</p>
</dd>
<dt>Lost in Translation: Modern Image Classifiers still degrade even under simple Translations.</dt><dd><p>Leander Kurscheidt, Matthias Hein.</p>
</dd>
<dt>SI-Score.</dt><dd><p>Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai.</p>
</dd>
<dt>The Semantic Shift Benchmark.</dt><dd><p>Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman.</p>
</dd>
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<h2>Track 2: Extended abstracts<a class="headerlink" href="#track-2-extended-abstracts" title="Permalink to this heading">#</a></h2>
<dl class="simple">
<dt>Growing ObjectNet: Adding speech, VQA, occlusion, and measuring dataset difficulty.</dt><dd><p>David Mayo, David Lu, Chris Zhang, Jesse Cummings, Xinyu Lin, Boris Katz, James R. Glass, Andrei Barbu.</p>
</dd>
<dt>OOD-CV: A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts.</dt><dd><p>Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski.</p>
</dd>
<dt>Towards Systematic Robustness for Scalable Visual Recognition.</dt><dd><p>Mohamed Omran, Bernt Schiele.</p>
</dd>
<dt>Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time.</dt><dd><p>Huaxiu Yao, Caroline Choi, Yoonho Lee, Pang Wei Koh, Chelsea Finn.</p>
</dd>
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