activity
20222024
most citedTowards Adversarially Robust Deep Image Denoising

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2024

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…

cs.CV2023

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…

eess.SP2023

Deep Unrolling for Nonconvex Robust Principal Component Analysis

Elizabeth Z. C. Tan, Caroline Chaux, Emmanuel Soubies +1

We design algorithms for Robust Principal Component Analysis (RPCA) which consists in decomposing a matrix into the sum of a low rank matrix and a sparse matrix. We propose a deep…

cs.CV2023

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…

cs.CV2023

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…

eess.IV20221 cited

Towards Adversarially Robust Deep Image Denoising

Hanshu Yan, Jingfeng Zhang, Jiashi Feng +2

This work systematically investigates the adversarial robustness of deep image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from noisy observations degraded by…