Search Results for author:
Found 12 papers, 3 papers with code
Cross-view Masked Diffusion Transformers for Person Image Synthesis
Our best model surpasses the pixel-based diffusion with $\frac{2}{3}$ of the parameters and achieves $5. 43 \times$ faster inference.
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
It is well known the adversarial optimization of GAN-based image super-resolution (SR) methods makes the preceding SR model generate unpleasant and undesirable artifacts, leading to large distortion.
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
Self-Supervised Visual Representation Learning via Residual Momentum
To solve this problem, we propose "residual momentum" to directly reduce this gap to encourage the student to learn the representation as close to that of the teacher as possible, narrow the performance gap with the teacher, and significantly improve the existing SSL.
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo
Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS).
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
This yields a unified perspective on how negative samples and SimSiam alleviate collapse.
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL
Towards avoiding collapse in self-supervised learning (SSL), contrastive loss is widely used but often requires a large number of negative samples.
Self-supervised Learning with Local Attention-Aware Feature
In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features.
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.
Ranked #172 on
Object Detection
on COCO test-dev
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues.
