10 citations · 27 across the 13 of their papers we have counts for
6 papers · 1 filter
Does VLM Classification Benefit from LLM Description Semantics?
Pingchuan Ma, Lennart Rietdorf, Dmytro Kotovenko +2
Accurately describing images with text is a foundation of explainable AI. Vision-Language Models (VLMs) like CLIP have recently addressed this by aligning images and texts in a sha…
ROICtrl: Boosting Instance Control for Visual Generation
Yuchao Gu, Yipin Zhou, Yunfan Ye +5
Natural language often struggles to accurately associate positional and attribute information with multiple instances, which limits current text-based visual generation models to s…
WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians
Dmytro Kotovenko, Olga Grebenkova, Nikolaos Sarafianos +8
While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches de…
Diffusion Models and Representation Learning: A Survey
Michael Fuest, Pingchuan Ma, Ming Gui +3
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised lear…
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui +4
The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long…
DepthFM: Fast Monocular Depth Estimation with Flow Matching
Ming Gui, Johannes Schusterbauer, Ulrich Prestel +6
Current discriminative depth estimation methods often produce blurry artifacts, while generative approaches suffer from slow sampling due to curvatures in the noise-to-depth transp…