Rolling Shutter Correction
90 papers with code • 0 benchmarks • 0 datasets
Rolling Shutter Correction
Benchmarks
These leaderboards are used to track progress in Rolling Shutter Correction
Most implemented papers
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
hmgoforth/PointNetLK
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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
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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
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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.
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention
fpv-iplab/rulstm
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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
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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.
SMUG: Towards robust MRI reconstruction by smoothed unrolling
lgm70/smug
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To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation.
Rolling Shutter Correction with Intermediate Distortion Flow Estimation
Additionally, a multi-distortion flow prediction strategy is integrated to mitigate the issue of inaccurate flow estimation further.
