1 citations · 2 across the 11 of their papers we have counts for
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Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment
Bac Nguyen, Yuhta Takida, Naoki Murata +4
Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between obj…
PAVAS: Physics-Aware Video-to-Audio Synthesis
Oh Hyun-Bin, Yuhta Takida, Toshimitsu Uesaka +2
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing vis…
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
Naoki Murata, Chieh-Hsin Lai, Yuhta Takida +4
Recent literature has effectively leveraged diffusion models trained on continuous variables as priors for solving inverse problems. Notably, discrete diffusion models with discret…
PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao +5
The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffu…