Selected Paper Awards & Personal Awards

For more awards, please browse our news section.

All Publications

  1. 2024

    1. Y. Wang, Y. Jiang, Z. Hu, C. Ruhdorfer, M. Bâce, and A. Bulling, “VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour,” Proc. ACM on Human-Computer Interaction (PACM HCI), vol. 8, no. ETRA 239, Art. no. ETRA 239, Jul. 2024, doi: 10.1145/3655613.
    2. F. Huth, M. Koch, M. Awad-Mohammed, D. Weiskopf, and K. Kurzhals, “Eye Tracking on Text Reading with Visual Enhancements,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications. Glasgow, United Kingdom: Association for Computing Machinery, Jun. 2024, pp. 1–7. doi: 10.1145/3649902.3653521.
    3. S. A. Vriend, S. Vidyapu, K.-T. Chen, and D. Weiskopf, “Which Experimental Design is Better Suited for VQA Tasks? Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS). Jun. 2024. doi: 10.1145/3649902.3653519.
    4. Y. Wang et al., “SalChartQA: Question-driven Saliency on Information Visualisations,” in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI), in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI). ACM, May 2024, pp. 1--14. doi: 10.1145/3613904.3642942.
    5. V. Mikheev, R. Skukies, and B. Ehinger, “The Art of Brainwaves: A Survey on Event-Related Potential Visualization Practices,” Aperture Neuro, vol. 4, Apr. 2024, doi: 10.52294/001c.116386.
    6. M. Kurzweg, Y. Weiss, M. O. Ernst, A. Schmidt, and K. Wolf, “Survey on Haptic Feedback through Sensory Illusions in Interactive Systems,” ACM Comput. Surv., vol. 56, no. 8, Art. no. 8, Apr. 2024, doi: 10.1145/3648353.
    7. Y. Xue et al., “Reducing Ambiguities in Line-Based Density Plots by Image-Space Colorization,” IEEE Transactions on Visualization & Computer Graphics, vol. 30, no. 01, Art. no. 01, Jan. 2024, doi: 10.1109/TVCG.2023.3327149.
    8. D. Klötzl et al., “NMF-Based Analysis of Mobile Eye-Tracking Data,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications. <conf-loc>, <city>Glasgow</city>, <country>United Kingdom</country>, </conf-loc>: Association for Computing Machinery, 2024. doi: 10.1145/3649902.3653518.
    9. L. Joos, B. Jäckl, D. A. Keim, M. T. Fischer, L. Peska, and J. Lokoc, “Known-Item Search in Video: An Eye Tracking-Based Study,” in Proceedings of the 2024 International Conference on Multimedia Retrieval, in Proceedings of the 2024 International Conference on Multimedia Retrieval. New York, NY, USA: Association for Computing Machinery, 2024, pp. 311–319. doi: 10.1145/3652583.3658119.
    10. Y. Wang, Q. Dai, M. Bâce, K. Klein, and A. Bulling, “Saliency3D: a 3D Saliency Dataset Collected on Screen,” in Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), in Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA). ACM, 2024, pp. 1--6. doi: 10.1145/3649902.3653350.
    11. M. M. Hamza, E. Ullah, A. Baggag, H. Bensmail, M. Sedlmair, and M. Aupetit, “ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings,” Information Visualization, vol. 23, no. 2, Art. no. 2, 2024, doi: 10.1177/14738716231220536.
    12. T. Krake, D. Klötzl, D. Hägele, and D. Weiskopf, “Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–16, 2024, doi: 10.1109/TVCG.2024.3364388.
    13. P. Eades et al., “CelticGraph: Drawing Graphs as Celtic Knots and Links,” in Graph Drawing and Network Visualization, M. A. Bekos and M. Chimani, Eds., in Graph Drawing and Network Visualization. Cham: Springer Nature Switzerland, 2024, pp. 18--35. doi: 10.1007/978-3-031-49272-3_2.
    14. M. Koch, N. Pathmanathan, D. Weiskopf, and K. Kurzhals, “How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications. <conf-loc>, <city>Glasgow</city>, <country>United Kingdom</country>, </conf-loc>: Association for Computing Machinery, 2024. doi: 10.1145/3649902.3653349.
    15. Y. Zhang, H. Williams, F. Schreiber, and K. Klein, “Visualising the Invisible: Exploring Approaches for Visual Analysis of Dynamic Airflow in Geographic Environments Using Sensor Data,” in EuroVis Workshop on Visual Analytics (EuroVA), M. El-Assady and H.-J. Schulz, Eds., in EuroVis Workshop on Visual Analytics (EuroVA). The Eurographics Association, 2024. doi: 10.2312/eurova.20241117.
  2. 2023

    1. C. Beck and M. Köllner, “GHisBERT -- Training BERT from scratch for lexical semantic investigations across historical German language stages,” in Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, N. Tahmasebi, S. Montariol, H. Dubossarsky, A. Kutuzov, S. Hengchen, D. Alfter, F. Periti, and P. Cassotti, Eds., in Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change. Singapore: Association for Computational Linguistics, Dec. 2023, pp. 33--45. [Online]. Available: https://aclanthology.org/2023.lchange-1.4
    2. F. Heyen, Q. Q. Ngo, and M. Sedlmair, “Visual Overviews for Sheet Music Structure,” in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023. Nov. 2023, pp. 692--699. doi: doi.org/10.48550/arXiv.2308.06140.
    3. J. Schmalfuss, L. Mehl, and A. Bruhn, “Distracting Downpour: Adversarial Weather Attacks for Motion Estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Oct. 2023, pp. 10106–10116. [Online]. Available: https://openaccess.thecvf.com/content/ICCV2023/html/Schmalfuss_Distracting_Downpour_Adversarial_Weather_Attacks_for_Motion_Estimation_ICCV_2023_paper.html
    4. L. Hirsch, F. Müller, F. Chiossi, T. Benga, and A. M. Butz, “My Heart Will Go On: Implicitly Increasing Social Connectedness by Visualizing Asynchronous Players’ Heartbeats in VR Games,” Proc. ACM Hum.-Comput. Interact., vol. 7, no. CHI PLAY, Art. no. CHI PLAY, Oct. 2023, doi: 10.1145/3611057.
    5. J. Zagermann, S. Hubenschmid, D. Fink, J. Wieland, H. Reiterer, and T. Feuchtner, “Challenges and Opportunities for Collaborative Immersive Analytics with Hybrid User Interfaces,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). Los Alamitos, CA, USA: IEEE Computer Society, Oct. 2023, pp. 191–195. doi: 10.1109/ISMAR-Adjunct60411.2023.00044.
    6. O. Wiedemann, V. Hosu, S. Su, and D. Saupe, “Konx: cross-resolution image quality assessment,” Quality and User Experience, vol. 8, no. 8, Art. no. 8, Aug. 2023, doi: 10.1007/s41233-023-00061-8.
    7. E. Sood, L. Shi, M. Bortoletto, Y. Wang, P. Müller, and A. Bulling, “Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention,” in Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci), in Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci). Jul. 2023, pp. 3639–3646. [Online]. Available: https://escholarship.org/uc/item/5968p71m
    8. M. Becher, M. Heinemann, T. Marmann, G. Reina, D. Weiskopf, and T. Ertl, “Accelerated 2D visualization using adaptive resolution scaling and temporal reconstruction,” Journal of Visualization, Jul. 2023, doi: 10.1007/s12650-023-00925-3.
    9. L. Mehl, J. Schmalfuss, A. Jahedi, Y. Nalivayko, and A. Bruhn, “Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Jun. 2023, pp. 4981–4991. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html
    10. K.-T. Chen et al., “Gazealytics : A Unified and Flexible Visual Toolkit for Exploratory and Comparative Gaze Analysis,” in ETRA ’23 : Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, in ETRA ’23 : Proceedings of the 2023 Symposium on Eye Tracking Research and Applications. Association for Computing Machinery, May 2023, pp. 1–7. doi: 10.1145/3588015.3589844.
    11. M. Kern, S. Jaeger-Honz, F. Schreiber, and B. Sommer, “APL@voro—interactive visualization and analysis of cell membrane simulations,” Bioinformatics, vol. 39, no. 2, Art. no. 2, Feb. 2023, doi: 10.1093/bioinformatics/btad083.
    12. Y. Wang, M. Bâce, and A. Bulling, “Scanpath Prediction on Information Visualisations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1--15, Feb. 2023, doi: 10.1109/TVCG.2023.3242293.
    13. T. Kosch, J. Karolus, J. Zagermann, H. Reiterer, A. Schmidt, and P. W. Woźniak, “A Survey on Measuring Cognitive Workload in Human-Computer Interaction,” ACM Comput. Surv., Jan. 2023, doi: 10.1145/3582272.
    14. L. Mehl, A. Jahedi, J. Schmalfuss, and A. Bruhn, “M-FUSE: Multi-frame Fusion for Scene Flow Estimation,” in Proc. Winter Conference on Applications of Computer Vision (WACV), in Proc. Winter Conference on Applications of Computer Vision (WACV). Jan. 2023. doi: 10.48550/arXiv.2207.05704.
    15. N. Rodrigues, C. Schulz, S. Doring, D. Baumgartner, T. Krake, and D. Weiskopf, “Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, Jan. 2023, doi: 10.1109/TVCG.2022.3209429.
    16. M. Koch, K. Kurzhals, M. Burch, and D. Weiskopf, “Visualization Psychology for Eye Tracking Evaluation,” in Visualization Psychology, D. Albers Szafir, R. Borgo, M. Chen, D. J. Edwards, B. Fisher, and L. Padilla, Eds., in Visualization Psychology. , Cham: Springer International Publishing, 2023, pp. 243--260. doi: 10.1007/978-3-031-34738-2_10.
    17. W. Kerle-Malcharek, S. P. Feyer, F. Schreiber, and K. Klein, “GAV-VR: An Extensible Framework for Graph Analysis and Visualisation in Virtual Reality,” in ICAT-EGVE 2023 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, J.-M. Normand, M. Sugimoto, and V. Sundstedt, Eds., in ICAT-EGVE 2023 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments. The Eurographics Association, 2023. doi: 10.2312/egve.20231321.
    18. E. Pangratz, F. Chiossi, S. Villa, K. Gramann, and L. Gehrke, “Towards an Implicit Metric of Sensory-Motor Accuracy: Brain Responses to Auditory Prediction Errors in Pianists,” in Proceedings of the 15th Conference on Creativity and Cognition, in Proceedings of the 15th Conference on Creativity and Cognition. <conf-loc>, <city>Virtual Event</city>, <country>USA</country>, </conf-loc>: Association for Computing Machinery, 2023, pp. 129–138. doi: 10.1145/3591196.3593340.
    19. C. Morariu, A. Bibal, R. Cutura, B. Frénay, and M. Sedlmair, “Predicting User Preferences of Dimensionality Reduction Embedding Quality,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209449.
    20. M. Testolina, V. Hosu, M. Jenadeleh, D. Lazzarotto, D. Saupe, and T. Ebrahimi, “JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range,” in 15th International Conference on Quality of Multimedia Experience (QoMEX), in 15th International Conference on Quality of Multimedia Experience (QoMEX). 2023, pp. 55–60. doi: 10.1109/QoMEX58391.2023.10178554.
    21. M. Jenadeleh, J. Zagermann, H. Reiterer, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Relaxed forced choice improves performance of visual quality assessment methods,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX). 2023, pp. 37–42. doi: 10.1109/QoMEX58391.2023.10178467.
    22. J. Schmalfuss, E. Scheurer, H. Zhao, N. Karantzas, A. Bruhn, and D. Labate, “Blind image inpainting with sparse directional filter dictionaries for lightweight CNNs,” Journal of Mathematical Imaging and Vision (JMIV), vol. 65, pp. 323--339, 2023, doi: 10.1007/s10851-022-01119-6.
    23. K.-T. Chen et al., “Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology,” in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications. Tubingen, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3588015.3589841.
    24. P. Paetzold, R. Kehlbeck, H. Strobelt, Y. Xue, S. Storandt, and O. Deussen, “RectEuler: Visualizing Intersecting Sets using Rectangles,” Computer Graphics Forum, vol. 42, no. 3, Art. no. 3, 2023, doi: https://doi.org/10.1111/cgf.14814.
    25. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: https://doi.org/10.1007/s11242-023-02019-y.
    26. M. Gleicher, M. Riveiro, T. von Landesberger, O. Deussen, R. Chang, and C. Gillman, “A Problem Space for Designing Visualizations,” IEEE Computer Graphics and Applications, vol. 43, no. 4, Art. no. 4, 2023, doi: 10.1109/MCG.2023.3267213.
    27. S. Su et al., “Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model,” IEEE Transactions on Multimedia, vol. 26, pp. 2671–2685, 2023, doi: 10.1109/TMM.2023.3301276.
    28. T. Ge et al., “Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–13, 2023, doi: 10.1109/TVCG.2023.3284499.
    29. F. L. Dennig, M. Miller, D. A. Keim, and M. El-Assady, “FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–17, 2023, doi: 10.1109/TVCG.2023.3288356.
    30. J. Wieland, “Designing and Evaluating Interactions for Handheld AR,” in Companion Proceedings of the 2023 Conference on Interactive Surfaces and Spaces, in Companion Proceedings of the 2023 Conference on Interactive Surfaces and Spaces. <conf-loc>, <city>Pittsburgh</city>, <state>PA</state>, <country>USA</country>, </conf-loc>: Association for Computing Machinery, 2023, pp. 100–103. doi: 10.1145/3626485.3626555.
    31. A. Jahedi, M. Luz, M. Rivinius, L. Mehl, and A. Bruhn, “MS-RAFT+: High Resolution Multi-Scale RAFT,” International Journal of Computer Vision, pp. 1573–1405, 2023, doi: 10.1007/s11263-023-01930-7.
    32. A. V. Reinschluessel and J. Zagermann, “Exploring Hybrid User Interfaces for Surgery Planning,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2023, pp. 208–210. doi: 10.1109/ISMAR-Adjunct60411.2023.00048.
    33. M. Butt, L. Carnesale, and T. Ahmed, “Experiencers vs. agents in Urdu/Hindi nominalized verbs of perception,” in Proceedings of the Lexical Functional Grammar Conference, in Proceedings of the Lexical Functional Grammar Conference, vol. 28. 2023, pp. 90--113. [Online]. Available: https://lfg-proceedings.org/lfg/index.php/main/article/view/46
    34. W. Teramoto and M. O. Ernst, “Effects of invisible lip movements on phonetic perception,” Scientific Reports, vol. 13, no. 1, Art. no. 1, 2023, doi: 10.1038/s41598-023-33791-y.
    35. F. Draxler, A. Schmidt, and L. L. Chuang, “Relevance, Effort, and Perceived Quality: Language Learners’ Experiences with AI-Generated Contextually Personalized Learning Material,” in Proceedings of the 2023 ACM Designing Interactive Systems Conference, in Proceedings of the 2023 ACM Designing Interactive Systems Conference. <conf-loc>, <city>Pittsburgh</city>, <state>PA</state>, <country>USA</country>, </conf-loc>: Association for Computing Machinery, 2023, pp. 2249–2262. doi: 10.1145/3563657.3596112.
    36. M. Xue et al., “Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209371.
    37. N. Doerr, K. Angerbauer, M. Reinelt, and M. Sedlmair, “Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585816.
    38. D. Hägele, T. Krake, and D. Weiskopf, “Uncertainty-Aware Multidimensional Scaling,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209420.
    39. S. Hubenschmid, J. Zagermann, D. Leicht, H. Reiterer, and T. Feuchtner, “ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). New York, NY, USA: ACM, 2023. doi: https://doi.org/10.1145/3544548.3581438.
    40. A. Zaky, J. Zagermann, H. Reiterer, and T. Feuchtner, “Opportunities and Challenges of Hybrid User Interfaces for Optimization of Mixed Reality Interfaces,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2023, pp. 215–219. doi: 10.1109/ISMAR-Adjunct60411.2023.00050.
    41. S. Hubenschmid, D. I. Fink, J. Zagermann, J. Wieland, H. Reiterer, and T. Feuchtner, “Colibri: A Toolkit for Rapid Prototyping of Networking Across Realities,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2023, pp. 9–13. doi: 10.1109/ISMAR-Adjunct60411.2023.00010.
    42. C. Schneegass, M. L. Wilson, H. A. Maior, F. Chiossi, A. L. Cox, and J. Wiese, “The Future of Cognitive Personal Informatics,” in Proceedings of the 25th International Conference on Mobile Human-Computer Interaction, in Proceedings of the 25th International Conference on Mobile Human-Computer Interaction. Athens, Greece: Association for Computing Machinery, 2023. doi: 10.1145/3565066.3609790.
    43. X. Zhao et al., “CUDAS: Distortion-Aware Saliency Benchmark,” IEEE Access, vol. 11, pp. 58025–58036, 2023, doi: 10.1109/ACCESS.2023.3283344.
    44. G. Chen, H. Lin, O. Wiedemann, and D. Saupe, “Localization of Just Noticeable Difference for Image Compression,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX). 2023, pp. 61–66. doi: 10.1109/QoMEX58391.2023.10178653.
  3. 2022

    1. D. I. Fink, J. Zagermann, H. Reiterer, and H.-C. Jetter, “Re-Locations: Augmenting Personal and Shared Workspaces to Support Remote Collaboration in Incongruent Spaces,” Proc. ACM Hum.-Comput. Interact., vol. 6, no. ISS, Art. no. ISS, Nov. 2022, doi: 10.1145/3567709.
    2. K. Angerbauer and M. Sedlmair, “Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper),” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). Oct. 2022, pp. 20–27. doi: 10.1109/BELIV57783.2022.00007.
    3. C. Müller, M. Heinemann, D. Weiskopf, and T. Ertl, “Power Overwhelming: Quantifying the Energy Cost of Visualisation,” in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). Oct. 2022, pp. 38–46. doi: 10.1109/BELIV57783.2022.00009.
    4. J. Schmalfuss, P. Scholze, and A. Bruhn, “A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow,” Proceedings of the European Conference on Computer Vision (ECCV), Oct. 2022.
    5. A. Jahedi, L. Mehl, M. Rivinius, and A. Bruhn, “Multi-Scale RAFT: combining hierarchical concepts for learning-based optical flow estimation,” Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1236–1240, Oct. 2022, doi: 10.1109/ICIP46576.2022.9898048.
    6. H. Lin, H. Men, Y. Yan, J. Ren, and D. Saupe, “Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, Sep. 2022, pp. 1--4. doi: 10.1109/QoMEX55416.2022.9900904.
    7. P. Schäfer, N. Rodrigues, D. Weiskopf, and S. Storandt, “Group Diagrams for Simplified Representation of Scanpaths,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI). ACM, Aug. 2022. doi: 10.1145/3554944.3554971.
    8. S. Dosdall, K. Angerbauer, L. Merino, M. Sedlmair, and D. Weiskopf, “Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards,” in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction, M. Burch, G. Wallner, and D. Limberger, Eds., in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction. ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
    9. M. Zameshina et al., “Fairness in generative modeling: do it unsupervised!,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, in Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, Jul. 2022, pp. 320--323. doi: 10.1145/3520304.3528992.
    10. Y. Wang, M. Koch, M. Bâce, D. Weiskopf, and A. Bulling, “Impact of Gaze Uncertainty on AOIs in Information Visualisations,” in 2022 Symposium on Eye Tracking Research and Applications, in 2022 Symposium on Eye Tracking Research and Applications. ACM, Jun. 2022, pp. 1–6. doi: 10.1145/3517031.3531166.
    11. P. Balestrucci, D. Wiebusch, and M. O. Ernst, “ReActLab: A Custom Framework for Sensorimotor Experiments ‘in-the-wild,’” Frontiers in Psychology, vol. 13, Jun. 2022, doi: 10.3389/fpsyg.2022.906643.
    12. M. Koch, D. Weiskopf, and K. Kurzhals, “A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, vol. 5, no. 2, Art. no. 2, May 2022, doi: 10.1145/3530795.
    13. M. Philipp, N. Bacher, S. Sauer, F. Mathis-Ullrich, and A. Bruhn, “From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization,” in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, Mar. 2022, pp. 1–5. doi: 10.1109/ISBI52829.2022.9761704.
    14. F. Petersen, B. Goldluecke, O. Deussen, and H. Kuehne, “Style Agnostic 3D Reconstruction via Adversarial Style Transfer,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, Jan. 2022, pp. 2273–2282. doi: 10.1109/WACV51458.2022.00233.
    15. C. Schneegass, V. Füseschi, V. Konevych, and F. Draxler, “Investigating the Use of Task Resumption Cues to Support Learning in Interruption-Prone Environments,” Multimodal Technologies and Interaction, vol. 6, no. 1, Art. no. 1, 2022, doi: 10.3390/mti6010002.
    16. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
    17. F. Chiossi et al., “Adapting visualizations and interfaces to the user,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0035.
    18. F. Petersen, B. Goldluecke, C. Borgelt, and O. Deussen, “GenDR: A Generalized Differentiable Renderer,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). 2022, pp. 3992–4001. doi: 10.1109/CVPR52688.2022.00397.
    19. H. Lin et al., “Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 9, Art. no. 9, 2022, doi: 10.1109/TCSVT.2022.3163860.
    20. Y. Wang, C. Jiao, M. Bâce, and A. Bulling, “VisRecall: Quantifying Information Visualisation Recallability Via Question Answering,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, Art. no. 12, 2022, doi: 10.1109/TVCG.2022.3198163.
    21. L. Joos, S. Jaeger-Honz, F. Schreiber, D. A. Keim, and K. Klein, “Visual Comparison of Networks in VR,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 11, Art. no. 11, 2022, doi: 10.1109/TVCG.2022.3203001.
    22. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209427.
    23. T. Krake, A. Bruhn, B. Eberhardt, and D. Weiskopf, “Efficient and Robust Background Modeling with Dynamic Mode Decomposition,” Journal of Mathematical Imaging and Vision (2022), 2022, doi: 10.1007/s10851-022-01068-0.
    24. T. Krake, D. Klötzl, B. Eberhardt, and D. Weiskopf, “Constrained Dynamic Mode Decomposition,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209437.
    25. F. Chiossi, R. Welsch, S. Villa, L. Chuang, and S. Mayer, “Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience,” Big Data and Cognitive Computing, vol. 6, no. 2, Art. no. 2, 2022, doi: 10.3390/bdcc6020055.
    26. A. Niarakis et al., “Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology,” Briefings in bioinformatics, vol. 23, no. 4, Art. no. 4, 2022, doi: 10.1093/bib/bbac212.
    27. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0048.
    28. Y. Zhang, K. Klein, O. Deussen, T. Gutschlag, and S. Storandt, “Robust Visualization of Trajectory Data,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0036.
    29. Q. Q. Ngo, F. L. Dennig, D. A. Keim, and M. Sedlmair, “Machine Learning Meets Visualization – Experiences and Lessons Learned,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0034.
    30. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, and D. Weiskopf, “Scalability in Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–15, 2022, doi: 10.1109/TVCG.2022.3231230.
    31. D. Bienroth et al., “Spatially resolved transcriptomics in immersive environments,” Visual Computing for Industry, Biomedicine, and Art, vol. 5, no. 1, Art. no. 1, 2022, doi: 10.1186/s42492-021-00098-6.
    32. K. Klein, M. Sedlmair, and F. Schreiber, “Immersive Analytics: An Overview,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0037.
    33. J. Görtler et al., “Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, 2022, pp. 1–13. doi: 10.1145/3491102.3501823.
    34. P. Fleck, A. Sousa Calepso, S. Hubenschmid, M. Sedlmair, and D. Schmalstieg, “RagRug: A Toolkit for Situated Analytics,” IEEE Transactions on Visualization and Computer Graphics, 2022, doi: 10.1109/TVCG.2022.3157058.
    35. J. Schmalfuss, L. Mehl, and A. Bruhn, “Attacking Motion Estimation with Adversarial Snow,” in Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW), in Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW). 2022. doi: 10.48550/arXiv.2210.11242.
    36. N. Rodrigues, L. Shao, J. J. Yan, T. Schreck, and D. Weiskopf, “Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection,” in 2022 Symposium on Eye Tracking Research and Applications, in 2022 Symposium on Eye Tracking Research and Applications. Seattle, WA, USA: Association for Computing Machinery, 2022, pp. 59:1-59:7. doi: 10.1145/3517031.3531165.
    37. M. Becher et al., “Situated Visual Analysis and Live Monitoring for Manufacturing,” IEEE Computer Graphics and Applications, pp. 1–1, 2022, doi: 10.1109/MCG.2022.3157961.
    38. T. Krake, M. von Scheven, J. Gade, M. Abdelaal, D. Weiskopf, and M. Bischoff, “Efficient Update of Redundancy Matrices for Truss and Frame Structures,” Journal of Theoretical, Computational and Applied Mechanics, 2022, doi: 10.46298/jtcam.9615.
    39. D. Dietz et al., “Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR,” in 28th ACM Symposium on Virtual Reality Software and Technology, in 28th ACM Symposium on Virtual Reality Software and Technology. 2022, pp. 1--12. doi: 10.1145/3562939.3567818.
    40. T. Kosch, R. Welsch, L. Chuang, and A. Schmidt, “The Placebo Effect of Artificial Intelligence in Human-Computer Interaction,” ACM Transactions on Computer-Human Interaction, 2022, doi: 10.1145/3529225.
    41. J. Lou, H. Lin, D. Marshall, D. Saupe, and H. Liu, “TranSalNet: Towards perceptually relevant visual saliency prediction,” Neurocomputing, vol. 494, pp. 455–467, 2022, doi: https://doi.org/10.1016/j.neucom.2022.04.080.
    42. F. Götz-Hahn, V. Hosu, and D. Saupe, “Critical Analysis on the Reproducibility of Visual Quality Assessment Using Deep Features,” PLoS ONE, vol. 17, no. 8, Art. no. 8, 2022, doi: 10.1371/journal.pone.0269715.
    43. R. Kehlbeck, J. Görtler, Y. Wang, and O. Deussen, “SPEULER: Semantics-preserving Euler Diagrams,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, Art. no. 1, 2022, doi: 10.1109/TVCG.2021.3114834.
    44. K. Angerbauer et al., “Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, 2022. doi: 10.1145/3491102.3502133.
    45. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
    46. S. Hubenschmid et al., “ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies,” in CHI Conference on Human Factors in Computing Systems (CHI ’22), in CHI Conference on Human Factors in Computing Systems (CHI ’22). New York, NY: ACM, 2022, pp. 1–20. doi: 10.1145/3491102.3517550.
    47. D. Garkov, C. Müller, M. Braun, D. Weiskopf, and F. Schreiber, “Research Data Curation in Visualization: Position Paper,” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), M. Sedlmair, Ed., in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). 2022, pp. 56–65. doi: 10.1109/BELIV57783.2022.00011.
    48. V. Bruder, M. Larsen, T. Ertl, H. Childs, and S. Frey, “A Hybrid In Situ Approach for Cost Efficient Image Database Generation,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2022, doi: 10.1109/TVCG.2022.3169590.
    49. S. Frey et al., “Parameter Adaptation In Situ: Design Impacts and Trade-Offs,” in In Situ Visualization for Computational Science, H. Childs, J. C. Bennett, and C. Garth, Eds., in In Situ Visualization for Computational Science. Cham: Springer International Publishing, 2022, pp. 159--182. doi: 10.1007/978-3-030-81627-8_8.
    50. H. Tarner, V. Bruder, T. Ertl, S. Frey, and F. Beck, “Visually Comparing Rendering Performance from Multiple Perspectives,” in Vision, Modeling, and Visualization, J. Bender, M. Botsch, and D. Keim, Eds., in Vision, Modeling, and Visualization. The Eurographics Association, 2022. doi: 10.2312/vmv.20221211.
    51. A. Huang, P. Knierim, F. Chiossi, L. L. Chuang, and R. Welsch, “Proxemics for Human-Agent Interaction in Augmented Reality,” in CHI Conference on Human Factors in Computing Systems, in CHI Conference on Human Factors in Computing Systems. 2022, pp. 1--13. doi: 10.1145/3491102.3517593.
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  4. 2021

    1. C. Schulz et al., “Multi-Class Inverted Stippling,” ACM Trans. Graph., vol. 40, no. 6, Art. no. 6, Dec. 2021, doi: 10.1145/3478513.3480534.
    2. B. Roziere et al., “EvolGAN: Evolutionary Generative Adversarial Networks,” in Computer Vision -- ACCV 2020, in Computer Vision -- ACCV 2020. Cham: Springer International Publishing, Nov. 2021, pp. 679--694. doi: 10.1007/978-3-030-69538-5_41.
    3. K. Klein, D. Garkov, S. Rütschlin, T. Böttcher, and F. Schreiber, “QSDB—a graphical Quorum Sensing Database,” Database, vol. 2021, no. 2021, Art. no. 2021, Nov. 2021, doi: 10.1093/database/baab058.
    4. R. Sevastjanova, A.-L. Kalouli, C. Beck, H. Schäfer, and M. El-Assady, “Explaining Contextualization in Language Models using Visual Analytics,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Online: Association for Computational Linguistics, Aug. 2021, pp. 464--476. doi: 10.18653/v1/2021.acl-long.39.
    5. M. Aichem et al., “Visual exploration of large metabolic models,” Bioinformatics, vol. 37, no. 23, Art. no. 23, May 2021, doi: 10.1093/bioinformatics/btab335.
    6. P. Balestrucci, V. Maffei, F. Lacquaniti, and A. Moscatelli, “The Effects of Visual Parabolic Motion on the Subjective Vertical and on Interception,” Neuroscience, vol. 453, pp. 124–137, Jan. 2021, doi: 10.1016/j.neuroscience.2020.09.052.
    7. K. Lu et al., “Palettailor: Discriminable Colorization for Categorical Data,” IEEE Transactions on Visualization & Computer Graphics, vol. 27, no. 02, Art. no. 02, 2021, doi: 10.1109/TVCG.2020.3030406.
    8. S. Giebenhain and B. Goldlücke, “AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations,” in 2021 International Conference on 3D Vision (3DV), in 2021 International Conference on 3D Vision (3DV). 2021, pp. 1054–1064. doi: 10.1109/3DV53792.2021.00113.
    9. F. Draxler, C. Schneegass, J. Safranek, and H. Hussmann, “Why Did You Stop? - Investigating Origins and Effects of Interruptions during Mobile Language Learning,” in Mensch Und Computer 2021, in Mensch Und Computer 2021. Ingolstadt, Germany: Association for Computing Machinery, 2021, pp. 21–33. doi: 10.1145/3473856.3473881.
    10. K. Gadhave et al., “Predicting intent behind selections in scatterplot visualizations,” Information Visualization, vol. 20, no. 4, Art. no. 4, 2021, doi: 10.1177/14738716211038604.
    11. K. C. Kwan and H. Fu, “Automatic Image Checkpoint Selection for Guider-Follower Pedestrian Navigation,” Computer Graphics Forum, vol. 40, no. 1, Art. no. 1, 2021, doi: https://doi.org/10.1111/cgf.14192.
    12. C. Krauter, J. Vogelsang, A. S. Calepso, K. Angerbauer, and M. Sedlmair, “Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About COVID-19 Measures Using Gamification Elements,” in Mensch und Computer, in Mensch und Computer. ACM, 2021, pp. 593--596. doi: 10.1145/3473856.3474031.
    13. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” in Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), in Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Springer, 2021, pp. 140--152. doi: 10.1007/978-3-030-75549-2_12.
    14. F. Götz-Hahn, V. Hosu, H. Lin, and D. Saupe, “KonVid-150k : A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild,” IEEE Access, vol. 9, pp. 72139--72160, 2021, doi: 10.1109/ACCESS.2021.3077642.
    15. S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “STREAM: Exploring the Combination of Spatially-Aware Tablets with Augmented Reality Head-Mounted Displays for Immersive Analytics,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. , New York, NY, USA: Association for Computing Machinery, 2021. doi: 10.1145/3411764.3445298.
    16. B. Roziere et al., “Tarsier: Evolving Noise Injection in Super-Resolution GANs,” in 2020 25th International Conference on Pattern Recognition (ICPR), in 2020 25th International Conference on Pattern Recognition (ICPR). 2021, pp. 7028–7035. doi: 10.1109/ICPR48806.2021.9413318.
    17. K. Klein et al., “Visual analytics of sensor movement data for cheetah behaviour analysis,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00742-6.
    18. H. Booth and C. Beck, “Verb-second and Verb-first in the History of Icelandic,” Journal of Historical Syntax, vol. 5, no. 27, Art. no. 27, 2021, doi: 10.18148/hs/2021.v5i28.112.
    19. C. Morariu, A. Bibal, R. Cutura, B. Frenay, and M. Sedlmair, “DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality,” arXiv preprint, Technical Report arXiv:2105.09275, 2021. [Online]. Available: https://arxiv.org/abs/2105.09275
    20. G. J. Rijken et al., “Illegible Semantics: Exploring the Design Space of Metal Logos,” in IEEE VIS alt.VIS Workshop, in IEEE VIS alt.VIS Workshop. 2021. [Online]. Available: https://arxiv.org/abs/2109.01688
    21. H. Men, H. Lin, M. Jenadeleh, and D. Saupe, “Subjective Image Quality Assessment with Boosted Triplet Comparisons,” IEEE Access, vol. 9, pp. 138939–138975, 2021, doi: 10.1109/ACCESS.2021.3118295.
    22. H. Lin, G. Chen, and F. W. Siebert, “Positional Encoding: Improving Class-Imbalanced Motorcycle Helmet use Classification,” in 2021 IEEE International Conference on Image Processing (ICIP), in 2021 IEEE International Conference on Image Processing (ICIP). 2021, pp. 1194–1198. doi: 10.1109/ICIP42928.2021.9506178.
    23. S. Su, V. Hosu, H. Lin, Y. Zhang, and D. Saupe, “KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects,” in 32nd British Machine Vision Conference, in 32nd British Machine Vision Conference. 2021, pp. 1–12. [Online]. Available: https://www.bmvc2021-virtualconference.com/assets/papers/0868.pdf
    24. N. Grossmann, J. Bernard, M. Sedlmair, and M. Waldner, “Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation,” in IEEE Visualization Conference  (VIS, Short Paper), in IEEE Visualization Conference  (VIS, Short Paper). 2021, pp. 61--65. doi: 10.1109/VIS49827.2021.9623326.
    25. D. Bethge et al., “VEmotion: Using Driving Context for Indirect Emotion Prediction in Real-Time,” in The 34th Annual ACM Symposium on User Interface Software and Technology, in The 34th Annual ACM Symposium on User Interface Software and Technology. , New York, NY, USA: Association for Computing Machinery, 2021, pp. 638–651. doi: 10.1145/3472749.3474775.
    26. T. Müller, C. Schulz, and D. Weiskopf, “Adaptive Polygon Rendering for Interactive Visualization in the Schwarzschild Spacetime,” European Journal of Physics, vol. 43, no. 1, Art. no. 1, 2021, doi: 10.1088/1361-6404/ac2b36.
    27. K. Schatz et al., “2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution,” IEEE Computer Graphics and Applications, vol. 41, no. 6, Art. no. 6, 2021, doi: 10.1109/MCG.2020.3004613.
    28. L. Zhou, C. R. Johnson, and D. Weiskopf, “Data-Driven Space-Filling Curves,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030473.
    29. K. Klein, M. Aichem, Y. Zhang, S. Erk, B. Sommer, and F. Schreiber, “TEAMwISE : synchronised immersive environments for exploration and analysis of animal behaviour,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00746-2.
    30. J. Bernard, M. Hutter, M. Sedlmair, M. Zeppelzauer, and T. Munzner, “A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, no. 3–4, Art. no. 3–4, 2021, doi: 10.1145/3439333.
    31. Y. Chen, K. C. Kwan, L.-Y. Wei, and H. Fu, “Autocomplete Repetitive Stroking with Image Guidance,” in SIGGRAPH Asia 2021 Technical Communications, in SIGGRAPH Asia 2021 Technical Communications. Tokyo, Japan: Association for Computing Machinery, 2021. doi: 10.1145/3478512.3488595.
    32. J. Wieland, J. Zagermann, J. Müller, and H. Reiterer, “Separation, Composition, or Hybrid? : Comparing Collaborative 3D Object Manipulation Techniques for Handheld Augmented Reality,” in 2021 IEEE International Symposium on Mixed and Augmented Reality, in 2021 IEEE International Symposium on Mixed and Augmented Reality. Piscataway, NJ: IEEE, 2021, pp. 403--412. doi: 10.1109/ISMAR52148.2021.00057.
    33. T. Krake, S. Reinhardt, M. Hlawatsch, B. Eberhardt, and D. Weiskopf, “Visualization and Selection of Dynamic Mode Decomposition Components for Unsteady Flow,” Visual Informatics, vol. 5, no. 3, Art. no. 3, 2021, doi: 10.1016/j.visinf.2021.06.003.
    34. R. Cutura, C. Morariu, Z. Cheng, Y. Wang, D. Weiskopf, and M. Sedlmair, “Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves,” in The 14th International Symposium on Visual Information Communication and Interaction, in The 14th International Symposium on Visual Information Communication and Interaction. Potsdam, Germany: Association for Computing Machinery, 2021, p. 1:1—1:8. doi: 10.1145/3481549.3481569.
    35. F. Frieß, M. Braun, V. Bruder, S. Frey, G. Reina, and T. Ertl, “Foveated Encoding for Large High-Resolution Displays,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030445.
    36. R. Bian et al., “Implicit Multidimensional Projection of Local Subspaces,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030368.
    37. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “ProSeCo: Visual analysis of class separation measures and dataset characteristics,” Computers & Graphics, vol. 96, pp. 48–60, 2021, doi: https://doi.org/10.1016/j.cag.2021.03.004.
    38. M. Kraus et al., “Immersive Analytics with Abstract 3D Visualizations: A Survey,” Computer Graphics Forum, 2021, doi: https://doi.org/10.1111/cgf.14430.
    39. S. Hubenschmid, J. Zagermann, D. Fink, J. Wieland, T. Feuchtner, and H. Reiterer, “Towards Asynchronous Hybrid User Interfaces for Cross-Reality Interaction,” in ISS’21 Workshop Proceedings: “Transitional Interfaces in Mixed and Cross-Reality: A new frontier?,” H.-C. Jetter, J.-H. Schröder, J. Gugenheimer, M. Billinghurst, C. Anthes, M. Khamis, and T. Feuchtner, Eds., in ISS’21 Workshop Proceedings: “Transitional Interfaces in Mixed and Cross-Reality: A new frontier?” 2021. doi: 10.18148/kops/352-2-84mm0sggczq02.
    40. M. Kraus, K. Klein, J. Fuchs, D. A. Keim, F. Schreiber, and M. Sedlmair, “The Value of Immersive Visualization,” IEEE Computer Graphics and Applications (CG&A), vol. 41, no. 4, Art. no. 4, 2021, doi: 10.1109/MCG.2021.3075258.
    41. R. Cutura, K. Angerbauer, F. Heyen, N. Hube, and M. Sedlmair, “DaRt: Generative Art using Dimensionality Reduction Algorithms,” in 2021 IEEE VIS Arts Program (VISAP), in 2021 IEEE VIS Arts Program (VISAP). IEEE, 2021, pp. 59--72. doi: 10.1109/VISAP52981.2021.00013.
    42. C. Bu et al., “SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030404.
    43. F. Frieß, M. Becher, G. Reina, and T. Ertl, “Amortised Encoding for Large High-Resolution Displays,” in 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), in 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV). 2021, pp. 53–62. doi: 10.1109/LDAV53230.2021.00013.
    44. F. L. Dennig, M. T. Fischer, M. Blumenschein, J. Fuchs, D. A. Keim, and E. Dimara, “ParSetgnostics: Quality Metrics for Parallel Sets,” Computer Graphics Forum, vol. 40, no. 3, Art. no. 3, 2021, doi: https://doi.org/10.1111/cgf.14314.
    45. H. Ben Lahmar and M. Herschel, “Collaborative filtering over evolution provenance data for interactive visual data exploration,” Information Systems, vol. 95, p. 101620, 2021, doi: 10.1016/j.is.2020.101620.
    46. M. Burch, W. Huang, M. Wakefield, H. C. Purchase, D. Weiskopf, and J. Hua, “The State of the Art in Empirical User Evaluation of Graph Visualizations,” IEEE Access, vol. 9, pp. 4173–4198, 2021, doi: 10.1109/ACCESS.2020.3047616.
    47. K. Vock, S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “IDIAR : Augmented Reality Dashboards to Supervise Mobile Intervention Studies,” in Mensch und Computer 2021 (MuC ’21), in Mensch und Computer 2021 (MuC ’21). New York, NY: ACM, 2021. doi: 10.1145/3473856.3473876.
  5. 2020

    1. C. Beck, “DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings,” in Proceedings of the Fourteenth Workshop on Semantic Evaluation, in Proceedings of the Fourteenth Workshop on Semantic Evaluation. Barcelona (online): International Committee for Computational Linguistics, Dec. 2020, pp. 50--58. [Online]. Available: https://www.aclweb.org/anthology/2020.semeval-1.4
    2. C. Beck, H. Booth, M. El-Assady, and M. Butt, “Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias,” in Proceedings of the 14th Linguistic Annotation Workshop, in Proceedings of the 14th Linguistic Annotation Workshop. Barcelona, Spain: Association for Computational Linguistics, Dec. 2020, pp. 60--73. [Online]. Available: https://www.aclweb.org/anthology/2020.law-1.6
    3. M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Universität Konstanz, Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
    4. V. Bruder, C. Müller, S. Frey, and T. Ertl, “On Evaluating Runtime Performance of Interactive Visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, pp. 2848–2862, Sep. 2020, doi: 10.1109/TVCG.2019.2898435.
    5. M. Dias, D. Orellana, S. Vidal, L. Merino, and A. Bergel, “Evaluating a Visual Approach for Understanding JavaScript Source Code,” in Proceedings of the 28th International Conference on Program Comprehension, in Proceedings of the 28th International Conference on Program Comprehension. ACM, Jul. 2020, pp. 128–138. doi: https://doi.org/10.1145/3387904.3389275.
    6. A. Streichert, K. Angerbauer, M. Schwarzl, and M. Sedlmair, “Comparing Input Modalities for Shape Drawing Tasks,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP), in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP). ACM, 2020, pp. 1–5. doi: 10.1145/3379156.3391830.
    7. L. Zhou, M. Rivinius, C. R. Johnson, and D. Weiskopf, “Photographic High-Dynamic-Range Scalar Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 6, Art. no. 6, 2020, doi: 10.1109/TVCG.2020.2970522.
    8. O. Wiedemann, V. Hosu, H. Lin, and D. Saupe, “Foveated Video Coding for Real-Time Streaming Applications,” in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX). 2020, pp. 1–6. doi: 10.1109/QoMEX48832.2020.9123080.
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    20. R. Netzel, N. Rodrigues, A. Haug, and D. Weiskopf, “Compensation of Simultaneous Orientation Contrast in Superimposed Textures,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), A. Kerren, C. Hurter, and J. Braz, Eds., in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), vol. 3: IVAPP. SciTePress, 2019, pp. 48–57. doi: 10.5220/0007356800480057.
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    24. J. Görtler, M. Spicker, C. Schulz, D. Weiskopf, and O. Deussen, “Stippling of 2D Scalar Fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 6, Art. no. 6, 2019, doi: 10.1109/TVCG.2019.2903945.
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