Search Results for author:
Found 12 papers, 11 papers with code
Efficient Diffusion Model for Image Restoration by Residual Shifting
While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps.
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
Text-based diffusion models have exhibited remarkable success in generation and editing, showing great promise for enhancing visual content with their generative prior.
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps.
Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution (SR).
Exploring CLIP for Assessing the Look and Feel of Images
Measuring the perception of visual content is a long-standing problem in computer vision.
Ranked #9 on
Video Quality Assessment
on MSU SR-QA Dataset
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
With a simple yet effective motion-aware fully-connected network, SmoothNet improves the temporal smoothness of existing pose estimators significantly and enhances the estimation accuracy of those challenging frames as a side-effect.
HiNet: Deep Image Hiding by Invertible Network
Capacity, invisibility and security are three primary challenges in image hiding task.
Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video
In this paper, we focus on enhancing the perceptual quality of compressed video.
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
That is, there is not yet a general evaluation platform for research on multi-agent intelligence.
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i. e., playing games without extrinsic rewards but evaluated with extrinsic rewards.
Diversity-Driven Extensible Hierarchical Reinforcement Learning
However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach
Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
