10 citations · 27 across the 13 of their papers we have counts for
13 papers
ENCORE: Event-Assisted Complementary Motion Refinement for Learned Video Compression
Shuhan Ye, Hongbin Yu, Chenqi Kong +4
Learned video compression relies on accurate temporal modeling to remove redundancy between adjacent frames. However, most existing codecs infer motion solely from discretely sampl…
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
Yusong Li, Pingchuan Ma, Ming Gui +2
Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained p…
Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Johannes Schusterbauer, Ming Gui, Yusong Li +3
Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function evaluations. While convenient,…
A Cocktail-Party Benchmark: Multi-Modal dataset and Comparative Evaluation Results
Thai-Binh Nguyen, Katerina Zmolikova, Pingchuan Ma +3
We introduce the task of Multi-Modal Context-Aware Recognition (MCoRec) in the ninth CHiME Challenge, which addresses the cocktail-party problem of overlapping conversations in a s…
SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Pingchuan Ma, Xiaopei Yang, Yusong Li +4
Explicitly disentangling style and content in vision models remains challenging due to their semantic overlap and the subjectivity of human perception. Existing methods propose sep…
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…
Motion Flow Matching for Human Motion Synthesis and Editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma +7
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawba…
Boosting Latent Diffusion with Flow Matching
Johannes Schusterbauer, Ming Gui, Pingchuan Ma +4
Visual synthesis has recently seen significant leaps in performance, largely due to breakthroughs in generative models. Diffusion models have been a key enabler, as they excel in i…