Rolling Shutter Correction
94 papers with code • 1 benchmarks • 1 datasets
Rolling Shutter Correction
Benchmarks
These leaderboards are used to track progress in Rolling Shutter Correction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
BS-RSC
Most implemented papers
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
hmgoforth/PointNetLK
•
•
CVPR 2019
To date, the successful application of PointNet to point cloud registration has remained elusive.
Learned Primal-dual Reconstruction
We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Chefibadly/Language-translation-model-training-and-implementation-using-flask
•
•
Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides.
Real-time Power System State Estimation and Forecasting via Deep Neural Networks
LiangZhangUMN/PSSE-via-DNNs
•
•
To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring.
CLTune: A Generic Auto-Tuner for OpenCL Kernels
For matrix-multiplication, we use CLTune to explore a parameter space of more than two-hundred thousand configurations, we show the need for device-specific tuning, and outperform the clBLAS library on NVIDIA, AMD and Intel GPUs.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
We compare our model and learning procedure to other back-propagation through time alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization.
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
fpv-iplab/rulstm
•
•
ICCV 2019
Our method is ranked first in the public leaderboard of the EPIC-Kitchens egocentric action anticipation challenge 2019.
Unrolling Ternary Neural Networks
The computational complexity of neural networks for large scale or real-time applications necessitates hardware acceleration.
Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video
fpv-iplab/rulstm
•
•
The experiments show that the proposed architecture is state-of-the-art in the domain of egocentric videos, achieving top performances in the 2019 EPIC-Kitchens egocentric action anticipation challenge.
End-to-end reconstruction meets data-driven regularization for inverse problems
Subhadip-1/unrolling_meets_data_driven_regularization
•
•
NeurIPS 2021
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
