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Found 7 papers, 4 papers with code
Proximal Gradient Descent Unfolding Dense-spatial Spectral-attention Transformer for Compressive Spectral Imaging
PGDUDST requires only 58% of the training time of RDLUF-MixS^2-9stg to achieve comparable reconstruction results.
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Ranked #1 on
Blind Face Restoration
on LFW
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting.
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet
When applying deep learning into GISC spectral camera, there are several challenges need to be solved: 1) how to deal with the large amount of 3D hyperspectral data, 2) how to reduce the influence caused by the uncertainty of the random reference measurements, 3) how to improve the reconstructed image quality as far as possible.
FV-UPatches: Enhancing Universality in Finger Vein Recognition
In the proposed framework, the domain mapper is an approximation to a specific extraction function thus the training is only a one-time effort with limited data.
SRQA: Synthetic Reader for Factoid Question Answering
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
Meanwhile, we construct A3Net for the WebQA dataset.
