76 citations · 257 across the 98 of their papers we have counts for
15 papers · 1 filter
VeGaS: Video Gaussian Splatting
Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil +3
Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to tra…
Neural Surface Priors for Editable Gaussian Splatting
Jakub Szymkowiak, Weronika Jakubowska, Dawid Malarz +5
In computer graphics and vision, recovering easily modifiable scene appearance from image data is crucial for applications such as content creation. We introduce a novel method tha…
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz +3
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Adam Kania, Marko Mihajlovic, Sergey Prokudin +2
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multi…
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
Artur Kasymov, Marcin Sendera, Michał Stypułkowski +2
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the…
Make Interval Bound Propagation great again
Patryk Krukowski, Daniel Wilczak, Jacek Tabor +2
In various scenarios motivated by real life, such as medical data analysis, autonomous driving, and adversarial training, we are interested in robust deep networks. A network is ro…