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Found 32 papers, 19 papers with code
Consistent Subject Generation via Contrastive Instantiated Concepts
We introduce Contrastive Concept Instantiation (CoCoIns) to effectively synthesize consistent subjects across multiple independent creations.
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR).
KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities
Using KITTEN, we conduct a systematic study on the fidelity of entities in text-to-image generation models, focusing on their ability to generate a wide range of real-world visual entities, such as landmark buildings, aircraft, plants, and animals.
A Simple Approach to Unifying Diffusion-based Conditional Generation
Recent progress in image generation has sparked research into controlling these models through condition signals, with various methods addressing specific challenges in conditional generation.
Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration
As SE increases computational complexity during inference, we propose a re-boosting module to the SC (Reb-SC) to improve the SC strategy further by incorporating SE into SC without increasing inference time.
Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance
In subject-driven text-to-image synthesis, the synthesis process tends to be heavily influenced by the reference images provided by users, often overlooking crucial attributes detailed in the text prompt.
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
Towards this goal, we propose AdaIR, a novel framework that enables low storage cost and efficient training without sacrificing performance.
Instruct-Imagen: Image Generation with Multi-modal Instruction
We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision.
DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models
Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.
Multi-task Image Restoration Guided By Robust DINO Features
To guide the restoration model with the features of DINOv2, we develop a DINO-Restore adaption and fusion module to adjust the channel of fused features from PSF and then integrate them with the features from the restoration model.
Effective Adapter for Face Recognition in the Wild
Traditional heuristic approaches-either training models directly on these degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective, primarily due to the degradation of facial features and the discrepancy in image domains.
ProPainter: Improving Propagation and Transformer for Video Inpainting
We also propose a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens.
Ranked #1 on
Video Inpainting
on YouTube-VOS 2018
Dual Associated Encoder for Face Restoration
Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild.
Ranked #2 on
Blind Face Restoration
on WIDER
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).
Collaborative Diffusion for Multi-Modal Face Generation and Editing
In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training.
Identity Encoder for Personalized Diffusion
Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
This paper proposes a method for generating images of customized objects specified by users.
ReVersion: Diffusion-Based Relation Inversion from Images
In this work, we propose the Relation Inversion task, which aims to learn a specific relation (represented as "relation prompt") from exemplar images.
Reference-based Image and Video Super-Resolution via C2-Matching
To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution.
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond
While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN.
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 #3 on
No-Reference Image Quality Assessment
on UHD-IQA
No-Reference Image Quality Assessment
Video Quality Assessment
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
Ranked #1 on
Blind Face Restoration
on CelebA-Test
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
The exploitation of long-term information has been a long-standing problem in video restoration.
Investigating Tradeoffs in Real-World Video Super-Resolution
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training.
Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning
We address this problem from a new perspective, by jointly considering colorization and temporal consistency in a unified framework.
Robust Reference-based Super-Resolution via C2-Matching
However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e. g. scale and rotation) and the resolution gap (e. g. HR and LR).
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.
Ranked #1 on
Video Enhancement
on MFQE v2
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension.
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
We show that pre-trained Generative Adversarial Networks (GANs), e. g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR).
Understanding Deformable Alignment in Video Super-Resolution
Aside from the contributions to deformable alignment, our formulation inspires a more flexible approach to introduce offset diversity to flow-based alignment, improving its performance.
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
Ranked #2 on
Deblurring
on REDS
