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Found 24 papers, 16 papers with code
Weights Shuffling for Improving DPSGD in Transformer-based Models
Differential Privacy (DP) mechanisms, especially in high-dimensional settings, often face the challenge of maintaining privacy without compromising the data utility.
AceMap: Knowledge Discovery through Academic Graph
The exponential growth of scientific literature requires effective management and extraction of valuable insights.
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4.
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images.
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
Due to the extremely large image coverage area (e. g., thousands square kilometers), candidate targets in these images are much smaller, dimer, more changeable than those targets observed by aerial-based and land-based imaging devices.
Real-World Light Field Image Super-Resolution via Degradation Modulation
In our method, a practical LF degradation model is developed to formulate the degradation process of real LF images.
Occlusion-Aware Cost Constructor for Light Field Depth Estimation
Based on the proposed cost constructor, we develop a deep network for LF depth estimation.
Disentangling Light Fields for Super-Resolution and Disparity Estimation
In this paper, we propose a generic mechanism to disentangle these coupled information for LF image processing.
Dense Dual-Attention Network for Light Field Image Super-Resolution
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available.
Light Field Image Super-Resolution with Transformers
With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted.
Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution
In this letter, we propose a light field refocusing method to improve the imaging quality of camera arrays.
Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection
Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection.
Improved Matrix Gaussian Mechanism for Differential Privacy
The wide deployment of machine learning in recent years gives rise to a great demand for large-scale and high-dimensional data, for which the privacy raises serious concern.
Unsupervised Degradation Representation Learning for Blind Super-Resolution
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
Certified Distributional Robustness via Smoothed Classifiers
We focus on certified robustness of smoothed classifiers in this work, and propose to use the worst-case population loss over noisy inputs as a robustness metric.
Symmetric Parallax Attention for Stereo Image Super-Resolution
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used.
Certified Distributional Robustness on Smoothed Classifiers
For smoothed classifiers, we propose the worst-case adversarial loss over input distributions as a robustness certificate.
Parallax Attention for Unsupervised Stereo Correspondence Learning
Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.
Light Field Image Super-Resolution Using Deformable Convolution
In this paper, we propose a deformable convolution network (i. e., LF-DFnet) to handle the disparity problem for LF image SR.
Learning A Single Network for Scale-Arbitrary Super-Resolution
In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.
Spatial-Angular Interaction for Light Field Image Super-Resolution
Specifically, spatial and angular features are first separately extracted from input LFs, and then repetitively interacted to progressively incorporate spatial and angular information.
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet).
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs.
Learning Parallax Attention for Stereo Image Super-Resolution
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.
Ranked #1 on
Image Super-Resolution
on KITTI 2012 - 4x upscaling
