2 citations · 3 across the 10 of their papers we have counts for
10 papers
Rest2Visual: Predicting Visually Evoked fMRI from Resting-State Scans
Chuyang Zhou, Ziao Ji, Daochang Liu +3
Understanding how spontaneous brain activity relates to stimulus-driven neural responses is a fundamental challenge in cognitive neuroscience. While task-based functional magnetic…
Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility
Yutong Hao, Chen Chen, Ajmal Saeed Mian +2
Diffusion models can generate realistic videos, but existing methods rely on implicitly learning physical reasoning from large-scale text-video datasets, which is costly, difficult…
Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance
Ky Dan Nguyen, Hoang Lam Tran, Anh-Dung Dinh +4
Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies betwe…
Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models
Chen Chen, Daochang Liu, Mubarak Shah +1
Text-to-image diffusion models have demonstrated remarkable capabilities in creating images highly aligned with user prompts, yet their proclivity for memorizing training set image…
Generative Physical AI in Vision: A Survey
Daochang Liu, Junyu Zhang, Anh-Dung Dinh +5
Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication…
Investigating Memorization in Video Diffusion Models
Chen Chen, Enhuai Liu, Daochang Liu +2
Diffusion models, widely used for image and video generation, face a significant limitation: the risk of memorizing and reproducing training data during inference, potentially gene…
Exploring Local Memorization in Diffusion Models via Bright Ending Attention
Chen Chen, Daochang Liu, Mubarak Shah +1
Text-to-image diffusion models have achieved unprecedented proficiency in generating realistic images. However, their inherent tendency to memorize and replicate training data duri…
Compress Guidance in Conditional Diffusion Sampling
Anh-Dung Dinh, Daochang Liu, Chang Xu
We found that enforcing guidance throughout the sampling process is often counterproductive due to the model-fitting issue, where samples are 'tuned' to match the classifier's para…
Surgical Triplet Recognition via Diffusion Model
Daochang Liu, Axel Hu, Mubarak Shah +1
Surgical triplet recognition is an essential building block to enable next-generation context-aware operating rooms. The goal is to identify the combinations of instruments, verbs,…
Towards Memorization-Free Diffusion Models
Chen Chen, Daochang Liu, Chang Xu
Pretrained diffusion models and their outputs are widely accessible due to their exceptional capacity for synthesizing high-quality images and their open-source nature. The users,…