Search Results for author:
Found 15 papers, 9 papers with code
VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
Different from conventional T2V sampling (i. e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating.
QASE Enhanced PLMs: Improved Control in Text Generation for MRC
To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module.
Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discovery
Based on the discovered zero-cost proxy, we conduct a ViT architecture search in a training-free manner.
DreaMoving: A Human Video Generation Framework based on Diffusion Models
In this paper, we present DreaMoving, a diffusion-based controllable video generation framework to produce high-quality customized human videos.
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
Also, humans have brain regions dedicated to understanding the minds of others and analyzing their intentions, such as the medial prefrontal cortex of the temporal lobe.
Ranked #2 on
Skeleton Based Action Recognition
on NTU RGB+D 120
(using extra training data)
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Learning the Non-Differentiable Optimization for Blind Super-Resolution
Instead of considering iterative strategy, we make the blur kernel predictor trainable in the whole blind SR model, in which AMNet is well-trained.
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services.
Image Fine-grained Inpainting
Besides, we devise a geometrical alignment constraint item to compensate for the pixel-based distance between prediction features and ground-truth ones.
Ranked #1 on
Facial Inpainting
on FFHQ
Lightweight Image Super-Resolution with Information Multi-distillation Network
In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results.
Ranked #10 on
Image Super-Resolution
on Manga109 - 3x upscaling
Progressive Perception-Oriented Network for Single Image Super-Resolution
In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network.
Ranked #8 on
Image Super-Resolution
on Urban100 - 4x upscaling
(SSIM metric)
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 Accurate Single Image Super-Resolution via Information Distillation Network
Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution.
Ranked #4 on
Image Super-Resolution
on IXI
