6 citations · 7 across the 11 of their papers we have counts for
12 papers
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…
Guiding Token-Sparse Diffusion Models
Felix Krause, Stefan Andreas Baumann, Johannes Schusterbauer +4
Diffusion models deliver high quality in image synthesis but remain expensive during training and inference. Recent works have leveraged the inherent redundancy in visual content t…
Purrception: Variational Flow Matching for Vector-Quantized Image Generation
Răzvan-Andrei Matişan, Vincent Tao Hu, Grigory Bartosh +6
We introduce Purrception, a variational flow matching approach for vector-quantized image generation that provides explicit categorical supervision while maintaining continuous tra…
MaskFlow: Discrete Flows For Flexible and Efficient Long Video Generation
Michael Fuest, Vincent Tao Hu, Björn Ommer
Generating long, high-quality videos remains a challenge due to the complex interplay of spatial and temporal dynamics and hardware limitations. In this work, we introduce MaskFlow…
TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training
Felix Krause, Timy Phan, Ming Gui +3
Diffusion models have emerged as the mainstream approach for visual generation. However, these models typically suffer from sample inefficiency and high training costs. Consequentl…
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…
[MASK] is All You Need
Vincent Tao Hu, Björn Ommer
In generative models, two paradigms have gained attraction in various applications: next-set prediction-based Masked Generative Models and next-noise prediction-based Non-Autoregre…
Distillation of Diffusion Features for Semantic Correspondence
Frank Fundel, Johannes Schusterbauer, Vincent Tao Hu +1
Semantic correspondence, the task of determining relationships between different parts of images, underpins various applications including 3D reconstruction, image-to-image transla…
Scaling Image Tokenizers with Grouped Spherical Quantization
Jiangtao Wang, Zhen Qin, Yifan Zhang +4
Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a…
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…
Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
Stefan Andreas Baumann, Felix Krause, Michael Neumayr +4
Recent advances in text-to-image (T2I) diffusion models have significantly improved the quality of generated images. However, providing efficient control over individual subjects,…
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge
Eslam Abdelrahman, Liangbing Zhao, Vincent Tao Hu +3
Diffusion models break down the challenging task of generating data from high-dimensional distributions into a series of easier denoising steps. Inspired by this paradigm, we propo…