[{"publication_status":"accepted","_id":"7002","publisher":"IARIA","year":"2026","title":"A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models","language":[{"iso":"eng"}],"status":"public","keyword":["segmentation","model comparison","face parsing"],"author":[{"id":"252442","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0004-9798-1112/work/218229773","first_name":"Viktoriia","last_name":"Vovchenko","orcid":"0009-0004-9798-1112","full_name":"Vovchenko, Viktoriia"},{"first_name":"Sergej","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0005-6858-9813/work/218229775","id":"236164","full_name":"Schultenkämper, Sergej","orcid":"0009-0005-6858-9813","last_name":"Schultenkämper"},{"id":"241734","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-0826-0144/work/218229776","first_name":"Frederik","last_name":"Bäumer","orcid":"0000-0002-0826-0144","full_name":"Bäumer, Frederik"}],"date_updated":"2026-07-27T12:17:01Z","main_file_link":[{"url":"https://www.thinkmind.org/library/AIMEDIA/AIMEDIA_2026"}],"type":"conference","date_created":"2026-06-19T16:31:47Z","publication":"2026 The Second International Conference on AI-based Media Innovation","user_id":"220548","conference":{"location":"Nizza, Frankreich","start_date":"2026-07-05","name":"2026 The Second International Conference on AI-based Media Innovation","end_date":"2026-07-09"},"publication_identifier":{"isbn":["978-1-68558-403-0"]},"editor":[{"first_name":"Stephan","full_name":"Böhm, Stephan","last_name":"Böhm"},{"first_name":"Petra","last_name":"Ahrweiler","full_name":"Ahrweiler, Petra"},{"last_name":"Gutenberg","full_name":"Gutenberg, Johannes","first_name":"Johannes"},{"first_name":"Clement ","full_name":"Leung, Clement ","last_name":"Leung"}],"citation":{"chicago":"Vovchenko, Viktoriia, Sergej Schultenkämper, and Frederik Bäumer. “A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models.” In <i>2026 The Second International Conference on AI-Based Media Innovation</i>, edited by Stephan Böhm, Petra Ahrweiler, Johannes Gutenberg, and Clement  Leung. IARIA, n.d.","ama":"Vovchenko V, Schultenkämper S, Bäumer F. A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models. In: Böhm S, Ahrweiler P, Gutenberg J, Leung C, eds. <i>2026 The Second International Conference on AI-Based Media Innovation</i>. IARIA.","short":"V. Vovchenko, S. Schultenkämper, F. Bäumer, in: S. Böhm, P. Ahrweiler, J. Gutenberg, C. Leung (Eds.), 2026 The Second International Conference on AI-Based Media Innovation, IARIA, n.d.","alphadin":"<span style=\"font-variant:small-caps;\">Vovchenko, Viktoriia</span> ; <span style=\"font-variant:small-caps;\">Schultenkämper, Sergej</span> ; <span style=\"font-variant:small-caps;\">Bäumer, Frederik</span>: A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models. In: <span style=\"font-variant:small-caps;\">Böhm, S.</span> ; <span style=\"font-variant:small-caps;\">Ahrweiler, P.</span> ; <span style=\"font-variant:small-caps;\">Gutenberg, J.</span> ; <span style=\"font-variant:small-caps;\">Leung, C.</span> (Hrsg.): <i>2026 The Second International Conference on AI-based Media Innovation</i> : IARIA","mla":"Vovchenko, Viktoriia, et al. “A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models.” <i>2026 The Second International Conference on AI-Based Media Innovation</i>, edited by Stephan Böhm et al., IARIA.","bibtex":"@inproceedings{Vovchenko_Schultenkämper_Bäumer, title={A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models}, booktitle={2026 The Second International Conference on AI-based Media Innovation}, publisher={IARIA}, author={Vovchenko, Viktoriia and Schultenkämper, Sergej and Bäumer, Frederik}, editor={Böhm, Stephan and Ahrweiler, Petra and Gutenberg, Johannes and Leung, Clement Editors} }","apa":"Vovchenko, V., Schultenkämper, S., &#38; Bäumer, F. (n.d.). A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models. In S. Böhm, P. Ahrweiler, J. Gutenberg, &#38; C. Leung (Eds.), <i>2026 The Second International Conference on AI-based Media Innovation</i>. Nizza, Frankreich: IARIA.","ieee":"V. Vovchenko, S. Schultenkämper, and F. Bäumer, “A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models,” in <i>2026 The Second International Conference on AI-based Media Innovation</i>, Nizza, Frankreich."},"abstract":[{"lang":"eng","text":"Selecting an appropriate semantic segmentation model for a given application domain remains a challenging and time-consuming task for practitioners and researchers. This paper presents an interactive, web-based platform that enables side-by-side visual comparison of multiple neural network segmentation models applied to identical images. The system integrates three transformer-based segmentation models: a face-parsing network producing 19 semantic classes, a SegFormer-B3 clothing segmentation model with 18 classes, and a Mask2Former model for general-purpose scene segmentation spanning 150 ADE20K categories. Key contributions include side-by-side evaluation of model outputs across multiple architectures and image categories, with real-time segment highlighting and a scalable inference caching system that enables model comparisons without requiring repeated graphics processing unit (GPU) computation. The platform organizes a curated dataset of images under a hierarchical category taxonomy, supporting structured evaluation across demographic and contextual variables. As a practical use case, the system is applied within the ADRIAN project to assist in verifying identity consistency across images through segmentation-based analysis. The platform thus contributes a specialized artificial intelligence (AI) tool for systematic segmentation and object detection model evaluation within media analysis pipelines, where selecting appropriate models is a recurring challenge across tasks from identity verification to content moderation. It is available under https://github.com/vika-v-v/neural-networks-for-image-segmentation and designed to lower the barrier for comparative model evaluation in applied computer vision workflows."}]},{"main_file_link":[{"open_access":"1"}],"type":"conference","user_id":"243822","oa":"1","page":"1363-1371","has_accepted_license":"1","date_updated":"2026-03-17T15:29:27Z","issue":"366","place":"Bonn","citation":{"ama":"Sangel M, Bensch E, Brandt-Pook H, Röllke T, Markworth C. Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES. In: Gesellschaft für Informatik e.V., ed. <i>INFORMATIK 2025</i>. Bonn; 2025:1363-1371. doi:<a href=\"https://doi.org/10.18420/INF2025_121\">10.18420/INF2025_121</a>","chicago":"Sangel, Marius, Emilia Bensch, Hans Brandt-Pook, Timo Röllke, and Cedric Markworth. “Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES.” In <i>INFORMATIK 2025</i>, edited by Gesellschaft für Informatik e.V., 1363–71. Bonn, 2025. <a href=\"https://doi.org/10.18420/INF2025_121\">https://doi.org/10.18420/INF2025_121</a>.","alphadin":"<span style=\"font-variant:small-caps;\">Sangel, Marius</span> ; <span style=\"font-variant:small-caps;\">Bensch, Emilia</span> ; <span style=\"font-variant:small-caps;\">Brandt-Pook, Hans</span> ; <span style=\"font-variant:small-caps;\">Röllke, Timo</span> ; <span style=\"font-variant:small-caps;\">Markworth, Cedric</span>: Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES. In: <span style=\"font-variant:small-caps;\">Gesellschaft für Informatik e.V.</span> (Hrsg.): <i>INFORMATIK 2025</i>. Bonn, 2025, S. 1363–1371","bibtex":"@inproceedings{Sangel_Bensch_Brandt-Pook_Röllke_Markworth_2025, place={Bonn}, title={Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES}, DOI={<a href=\"https://doi.org/10.18420/INF2025_121\">10.18420/INF2025_121</a>}, number={366}, booktitle={INFORMATIK 2025}, author={Sangel, Marius and Bensch, Emilia and Brandt-Pook, Hans and Röllke, Timo and Markworth, Cedric}, editor={Gesellschaft für Informatik e.V.Editor}, year={2025}, pages={1363–1371} }","mla":"Sangel, Marius, et al. “Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES.” <i>INFORMATIK 2025</i>, edited by Gesellschaft für Informatik e.V., no. 366, 2025, pp. 1363–71, doi:<a href=\"https://doi.org/10.18420/INF2025_121\">10.18420/INF2025_121</a>.","short":"M. Sangel, E. Bensch, H. Brandt-Pook, T. Röllke, C. Markworth, in: Gesellschaft für Informatik e.V. (Ed.), INFORMATIK 2025, Bonn, 2025, pp. 1363–1371.","apa":"Sangel, M., Bensch, E., Brandt-Pook, H., Röllke, T., &#38; Markworth, C. (2025). Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES. In Gesellschaft für Informatik e.V. (Ed.), <i>INFORMATIK 2025</i> (pp. 1363–1371). Bonn. <a href=\"https://doi.org/10.18420/INF2025_121\">https://doi.org/10.18420/INF2025_121</a>","ieee":"M. Sangel, E. Bensch, H. Brandt-Pook, T. Röllke, and C. Markworth, “Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES,” in <i>INFORMATIK 2025</i>, Potsdam, 2025, no. 366, pp. 1363–1371."},"corporate_editor":["Gesellschaft für Informatik e.V."],"doi":"10.18420/INF2025_121","year":"2025","status":"public","file":[{"content_type":"application/pdf","file_size":14374281,"file_name":"Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen Ansätze und Ergebnisse aus dem Projekt TRACES.pdf","relation":"main_file","access_level":"open_access","file_id":"6236","creator":"msangel1","success":1,"date_updated":"2025-10-15T07:02:12Z","date_created":"2025-10-15T07:02:12Z"}],"language":[{"iso":"ger"}],"file_date_updated":"2025-10-15T07:02:12Z","date_created":"2025-10-15T07:05:27Z","publication":"INFORMATIK 2025","department":[{"_id":"4b2dc5c9-bee3-11eb-b75f-ecc80f94fb21"}],"author":[{"last_name":"Sangel","full_name":"Sangel, Marius","id":"243822","first_name":"Marius"},{"first_name":"Emilia","full_name":"Bensch, Emilia","last_name":"Bensch"},{"first_name":"Hans","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0002-6668-2684/work/194297832","id":"206531","full_name":"Brandt-Pook, Hans","orcid":"0009-0002-6668-2684","last_name":"Brandt-Pook"},{"last_name":"Röllke","full_name":"Röllke, Timo","first_name":"Timo"},{"full_name":"Markworth, Cedric","last_name":"Markworth","first_name":"Cedric"}],"quality_controlled":"1","conference":{"location":"Potsdam","start_date":"2025-09-16","name":"INFORMATIKFESTIVAL 2025","end_date":"2025-09-19"},"publication_identifier":{"unknown":["2944-7682"]},"title":"Automatisierte Erkennung von Störstoffen in Bioabfall mit maschinellem Lernen: Ansätze und Ergebnisse aus dem Projekt TRACES","_id":"6235","project":[{"name":"Institute for Data Science Solutions","_id":"f432a2ee-bceb-11ed-a251-a83585c5074d"}],"publication_status":"epub_ahead","tmp":{"name":"Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)","image":"/images/cc_by_sa.png","legal_code_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","short":"CC BY-SA (4.0)"},"urn":"urn:nbn:de:hbz:bi10-62353","keyword":["Machine Learning","Computer Vision","Instance Segmentation","CNNs","YOLACT","Data Augmentation","Waste Classification","Trash Detection","Biowaste Analysis"]}]