Search Results for author:
Found 15 papers, 9 papers with code
Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec
Despite their significant progress, we argue that their performance has been limited by the simple adoption of the design convention for forward adaptation: using only a single type of hyper latent representation, which does not provide sufficient contextual information, especially in the first modeling step.
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images.
Demystifying Randomly Initialized Networks for Evaluating Generative Models
Our results indicate that the features from random networks can evaluate generative models well similarly to those from trained networks, and furthermore, the two types of features can be used together in a complementary way.
Joint Global and Local Hierarchical Priors for Learned Image Compression
Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG.
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks
Recently, the vulnerability of deep image classification models to adversarial attacks has been investigated.
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack
In this paper, we study the structural vulnerability of deep learning-based action recognition models against the adversarial attack using the one frame attack that adds an inconspicuous perturbation to only a single frame of a given video clip.
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.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
SRZoo: An integrated repository for super-resolution using deep learning
In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place.
Image and Video Processing Multimedia
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers
In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow.
Fluid Dynamics Computational Physics
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications.
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers.
Ranked #32 on
Image Super-Resolution
on BSD100 - 4x upscaling
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths.
Ranked #33 on
Image Super-Resolution
on BSD100 - 4x upscaling
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively.
Ranked #56 on
Image Super-Resolution
on BSD100 - 4x upscaling
Generative adversarial network-based image super-resolution using perceptual content losses
In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion.
