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Found 15 papers, 12 papers with code
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild
In this paper, we propose FunnyNet-W, a model that relies on cross- and self-attention for visual, audio and text data to predict funny moments in videos.
See360: Novel Panoramic View Interpolation
We present See360, which is a versatile and efficient framework for 360 panoramic view interpolation using latent space viewpoint estimation.
Soft-IntroVAE for Continuous Latent space Image Super-Resolution
Continuous image super-resolution (SR) recently receives a lot of attention from researchers, for its practical and flexible image scaling for various displays.
Arbitrary point cloud upsampling via Dual Back-Projection Network
Point cloud upsampling is an approach to increase the density of the point cloud so that detailed geometric information can be restored.
Name Your Style: An Arbitrary Artist-aware Image Style Transfer
Moreover, it can mimic the styles of one or many artists to achieve attractive results, thus highlighting a promising direction in image style transfer.
Multiple Style Transfer via Variational AutoEncoder
Modern works on style transfer focus on transferring style from a single image.
Variational AutoEncoder for Reference based Image Super-Resolution
In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE).
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Deep Relighting Networks for Image Light Source Manipulation
Manipulating the light source of given images is an interesting task and useful in various applications, including photography and cinematography.
Ranked #2 on
Image Relighting
on VIDIT’20 validation set
DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting
Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas.
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
Depending upon whether using a discriminator or not, a deep convolutional neural network can provide an image with high fidelity or better perceptual quality.
Image Super-Resolution via Attention based Back Projection Networks
In this paper, we propose an Attention based Back Projection Network (ABPN) for image super-resolution.
Ranked #1 on
Image Super-Resolution
on Manga109 - 16x upscaling
Hierarchical Back Projection Network for Image Super-Resolution
We propose a Hierarchical Back Projection Network (HBPN), that cascades multiple HourGlass (HG) modules to bottom-up and top-down process features across all scales to capture various spatial correlations and then consolidates the best representation for reconstruction.
Ranked #4 on
Image Super-Resolution
on BSD100 - 8x upscaling
