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Found 6 papers, 5 papers with code
MapSeg: Segmentation guided structured model for online HD map construction
The development of online high-definition maps is significant since they provide real-time, accurate, and updatable geographic information for location-based applications, such as autonomous driving and intelligent transportation, thus improving the performance and reliability of these applications.
Cross-View Hierarchy Network for Stereo Image Super-Resolution
Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views.
Perceiving and Modeling Density is All You Need for Image Dehazing
However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.
Ranked #5 on
Image Dehazing
on Haze4k
SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring
On this basis, we reduce the number of up-sampling and down-sampling and design a simple network structure.
Ranked #1 on
Image Deblurring
on RealBlur-R(trained on GoPro)
InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT) devices has motivated a tremendous demand for automated solutions to enable fast development and deployment of efficient (1) DNNs equipped with instantaneous accuracy-efficiency trade-off capability to accommodate the time-varying resources at IoT devices and (2) dataflows to optimize DNNs' execution efficiency on different devices.
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification.
