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Found 13 papers, 11 papers with code
InstructRestore: Region-Customized Image Restoration with Human Instructions
To achieve this, we first develop a data generation engine to produce training triplets, each consisting of a high-quality image, the target region description, and the corresponding region mask.
Toward Generalizing Visual Brain Decoding to Unseen Subjects
Prior works typically focus on decoding brain activity of individuals based on the observation that different subjects exhibit different brain activities, while it remains unclear whether brain decoding can be generalized to unseen subjects.
Training on the Benchmark Is Not All You Need
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase.
A Preliminary Exploration Towards General Image Restoration
Existing deep models, tailored for specific individual image restoration tasks, often fall short in effectively addressing these challenges.
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models
The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain.
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up.
Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy
While single task image restoration (IR) has achieved significant successes, it remains a challenging issue to train a single model which can tackle multiple IR tasks.
HAT: Hybrid Attention Transformer for Image Restoration
In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
DegAE: A New Pretraining Paradigm for Low-Level Vision
However, we argue that pretraining is more significant for high-cost tasks, where data acquisition is more challenging.
Reflash Dropout in Image Super-Resolution
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR).
Efficient Image Super-Resolution Using Pixel Attention
Pixel attention (PA) is similar as channel attention and spatial attention in formulation.
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.
