26 citations · 92 across the 14 of their papers we have counts for
5 papers · 1 filter
LightningDrag: Lightning Fast and Accurate Drag-based Image Editing Emerging from Videos
Yujun Shi, Jun Hao Liew, Hanshu Yan +2
Accuracy and speed are critical in image editing tasks. Pan et al. introduced a drag-based image editing framework that achieves pixel-level control using Generative Adversarial Ne…
Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint Method
Jiachun Pan, Hanshu Yan, Jun Hao Liew +2
Training-free guided sampling in diffusion models leverages off-the-shelf pre-trained networks, such as an aesthetic evaluation model, to guide the generation process. Current trai…
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models
Jiachun Pan, Jun Hao Liew, Vincent Y. F. Tan +2
Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models (DPMs) with user-provided concepts. This paper aims…
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
Yujun Shi, Chuhui Xue, Jun Hao Liew +5
Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DragGAN is an interactive point-based image editing framew…
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Yujun Shi, Kuangqi Zhou, Jian Liang +5
Class Incremental Learning (CIL) aims at learning a multi-class classifier in a phase-by-phase manner, in which only data of a subset of the classes are provided at each phase. Pre…