Search Results for author:
Found 47 papers, 30 papers with code
Learning Inclusion Matching for Animation Paint Bucket Colorization
In this work, we introduce a new learning-based inclusion matching pipeline, which directs the network to comprehend the inclusion relationships between segments rather than relying solely on direct visual correspondences.
LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field.
Control Color: Multimodal Diffusion-based Interactive Image Colorization
We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring.
Towards Language-Driven Video Inpainting via Multimodal Large Language Models
We introduce a new task -- language-driven video inpainting, which uses natural language instructions to guide the inpainting process.
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.
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance
When combined with a diffusion prior, this partial guidance can deliver appealing results across a range of restoration tasks.
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
Adaptive Window Pruning for Efficient Local Motion Deblurring
Existing image deblurring methods predominantly focus on global deblurring, inadvertently affecting the sharpness of backgrounds in locally blurred images and wasting unnecessary computation on sharp pixels, especially for high-resolution images.
Flare7K++: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond
To address this issue, we additionally provide the annotations of light sources in Flare7K++ and propose a new end-to-end pipeline to preserve the light source while removing lens flares.
MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results
In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2023.
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).
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
MIPI 2023 Challenge on RGBW Remosaic: Methods and Results
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms.
MIPI 2023 Challenge on RGBW Fusion: Methods and Results
Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms.
Iterative Prompt Learning for Unsupervised Backlit Image Enhancement
To solve this issue, we devise a prompt learning framework that first learns an initial prompt pair by constraining the text-image similarity between the prompt (negative/positive sample) and the corresponding image (backlit image/well-lit image) in the CLIP latent space.
Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior
With the dataset, neural networks can be trained to remove the reflective flares effectively.
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns. Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance.
Deep Dynamic Scene Deblurring from Optical Flow
In this paper, we explore optical flow to remove dynamic scene blur by using the multi-scale spatially variant recurrent neural network (RNN).
Learning Dual Memory Dictionaries for Blind Face Restoration
Generally, it is a challenging and intractable task to improve the photo-realistic performance of blind restoration and adaptively handle the generic and specific restoration scenarios with a single unified model.
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
In this paper, we introduce, Flare7K, the first nighttime flare removal dataset, which is generated based on the observation and statistics of real-world nighttime lens flares.
Ranked #2 on
Flare Removal
on Flare7K
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results
In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022.
CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment
Our method inherits the zero-reference learning and curve-based framework from an effective low-light image enhancement method, Zero-DCE, with further speed up in its inference speed, reduction in its model size, and extension to controllable exposure adjustment.
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.
LEDNet: Joint Low-light Enhancement and Deblurring in the Dark
With the pipeline, we present the first large-scale dataset for joint low-light enhancement and deblurring.
Ranked #2 on
Low-Light Image Enhancement
on Sony-Total-Dark
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.
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.
Efficient Regional Memory Network for Video Object Segmentation
For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame.
Flexible Piecewise Curves Estimation for Photo Enhancement
This paper presents a new method, called FlexiCurve, for photo enhancement.
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block.
Cross-Scale Internal Graph Neural Network for Image Super-Resolution
Specifically, we dynamically construct a cross-scale graph by searching k-nearest neighboring patches in the downsampled LR image for each query patch in the LR image.
Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
A multi-scale context-aware fusion module is then introduced to adaptively select high-quality reconstructions for different parts from all coarse 3D volumes to obtain a fused 3D volume.
Ranked #3 on
3D Object Reconstruction
on Data3D−R2N2
GRNet: Gridding Residual Network for Dense Point Cloud Completion
In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information.
Ranked #3 on
Point Cloud Completion
on Completion3D
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation
In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework.
Toward 3D Object Reconstruction from Stereo Images
Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem.
Spatio-Temporal Filter Adaptive Network for Video Deblurring
To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework.
Ranked #3 on
Deblurring
on DVD
(using extra training data)
DAVANet: Stereo Deblurring with View Aggregation
Nowadays stereo cameras are more commonly adopted in emerging devices such as dual-lens smartphones and unmanned aerial vehicles.
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e. g., table legs) from different coarse 3D volumes to obtain a fused 3D volume.
Ranked #4 on
3D Object Reconstruction
on Data3D−R2N2
Deep Saliency Hashing
As the core of DSaH, the saliency loss guides the attention network to mine discriminative regions from pairs of images.
