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Found 13 papers, 5 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models
We utilize user history as in-context user preferences to address the first challenge.
Swift Parameter-free Attention Network for Efficient Super-Resolution
To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality.
Ranked #37 on
Image Super-Resolution
on Set14 - 4x upscaling
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
ISTA-Inspired Network for Image Super-Resolution
Inspired by the mathematical analysis, the ISTA block is developed to conduct the optimization in an end-to-end manner.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution
As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years.
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
Image super-resolution (SR) has been widely investigated in recent years.
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth.
Cross-SRN: Structure-Preserving Super-Resolution Network with Cross Convolution
It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details.
TotalRecall: A Bidirectional Candidates Generation Framework for Large Scale Recommender \& Advertising Systems
Recommender (RS) and Advertising/Marketing Systems (AS) play the key roles in E-commerce companies like Amazaon and Alibaba.
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution
Based on the observation, in this paper, we build a sequential hierarchical learning super-resolution network (SHSR) for effective image SR.
Ranked #9 on
Image Super-Resolution
on Manga109 - 3x upscaling
Iterative Network for Image Super-Resolution
A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
