@inproceedings{7002,
  abstract     = {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.},
  author       = {Vovchenko, Viktoriia and Schultenkämper, Sergej and Bäumer, Frederik},
  booktitle    = {2026 The Second International Conference on AI-based Media Innovation},
  keywords     = {segmentation, model comparison, face parsing},
  location     = {Nizza, Frankreich},
  publisher    = {IARIA},
  title        = {{A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models}},
  year         = {2026},
}

@inproceedings{6998,
  author       = {Vovchenko, Viktoriia and Barberi, Vincenzo and Schultenkämper, Sergej and Bäumer, Frederik},
  booktitle    = {2026 The First International Conference on Security and Cybersecurity in the AI and Digital Context},
  isbn         = {978-1-68558-449-8},
  keywords     = {Deepfakes, Image forensics, Diffusion models, Computer vision},
  location     = {Porto, Portugal},
  title        = {{ADRIAN InstructFace-Edit - Towards Robust Detection of AI-Manipulated Face Images}},
  year         = {2026},
}

@inproceedings{5407,
  author       = {Schultenkämper, Sergej and Vovchenko, Viktoriia and Bäumer, Frederik},
  booktitle    = {The 13th IEEE International Workshop on Semantic Computing for Social Networking: from user information to social knowledge and ethical AI},
  location     = {Laguna Hills, CA},
  title        = {{Enhancing Social Media Summarization with Instruction Tuned Large Language Models and Adaptive Summary Ranking}},
  doi          = {10.1109/ICSC64641.2025.00045},
  year         = {2025},
}