3 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Boosting Diffusion Models with an Adaptive Momentum Sampler
Xiyu Wang, Anh-Dung Dinh, Daochang Liu +1
Diffusion probabilistic models (DPMs) have been shown to generate high-quality images without the need for delicate adversarial training. However, the current sampling process in D…
Diffusion Action Segmentation
Daochang Liu, Qiyue Li, AnhDung Dinh +3
Temporal action segmentation is crucial for understanding long-form videos. Previous works on this task commonly adopt an iterative refinement paradigm by using multi-stage models.…