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Found 27 papers, 15 papers with code
Learning Factorized Weight Matrix for Joint Image Filtering
In this work, we propose to learn the weight matrix for joint image filtering.
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition
For the first time, we introduce vision Transformers into PPAR by treating a video as a tubelet sequence, and accordingly design two complementary mechanisms, i. e., sparsification and anonymization, to remove privacy from a spatio-temporal perspective.
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization.
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation
To this end, we propose CLIP-FLow, a semi-supervised iterative pseudo-labeling framework to transfer the pretraining knowledge to the target real domain.
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.
Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond with Cylinder-Style Convolutions
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a consistent and seamless cylinder.
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.
Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
Video Frame Interpolation Transformer
Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.
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.
The Nuts and Bolts of Adopting Transformer in GANs
Transformer becomes prevalent in computer vision, especially for high-level vision tasks.
GTT-Net: Learned Generalized Trajectory Triangulation
We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry.
3D Human Texture Estimation from a Single Image with Transformers
We propose a Transformer-based framework for 3D human texture estimation from a single image.
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.
3D Human Pose, Shape and Texture from Low-Resolution Images and Videos
Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications.
Exploiting Raw Images for Real-Scene Super-Resolution
In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images.
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
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).
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications, from activity recognition to creating virtual avatars.
Ranked #17 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PA-MPJPE metric)
Quadratic video interpolation
Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors.
Discrete Laplace Operator Estimation for Dynamic 3D Reconstruction
We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution.
Towards Real Scene Super-Resolution with Raw Images
Most existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input.
Learning Deformable Kernels for Image and Video Denoising
Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input.
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement
While significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs) extracting absolute features, such as edges and textures, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent methods.
Rendering Portraitures from Monocular Camera and Beyond
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
Learning to Super-Resolve Blurry Face and Text Images
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.
