3 papers
cs.CV2026
PiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation
Miłosz Adamczyk, Tymoteusz Zapala, Piotr Borycki +1
With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpretability is crucial to build…
cs.GR2026
FaceParts: Segmentation and Editing of Gaussian Splatting
Tymoteusz Zapała, Julia Farganus, Dominik Galus +3
Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generative models in the 2D image dom…
cs.CV2026
XSPLAIN: XAI-enabling Splat-based Prototype Learning for Attribute-aware INterpretability
Dominik Galus, Julia Farganus, Tymoteusz Zapala +4
3D Gaussian Splatting (3DGS) has rapidly become a standard for high-fidelity 3D reconstruction, yet its adoption in multiple critical domains is hindered by the lack of interpretab…