103 citations · 119 across the 7 of their papers we have counts for
6 papers · 1 filter
Improving Tuning-Free Real Image Editing with Proximal Guidance
Ligong Han, Song Wen, Qi Chen +13
DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifie…
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
Jierun Chen, Shiu-hong Kao, Hao He +4
To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does no…
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Qilong Zhangli, Jingru Yi, Di Liu +10
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, t…
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification
Haoyue Bai, Song Wen, S. -H. Gary Chan
Labeled crowd scene images are expensive and scarce. To significantly reduce the requirement of the labeled images, we propose ColorCount, a novel CNN-based approach by combining s…
Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty
Jierun Chen, Song Wen, S. -H. Gary Chan
Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this pape…
Crowd Counting on Images with Scale Variation and Isolated Clusters
Haoyue Bai, Song Wen, S. -H. Gary Chan
Crowd counting is to estimate the number of objects (e.g., people or vehicles) in an image of unconstrained congested scenes. Designing a general crowd counting algorithm applicabl…