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Found 10 papers, 2 papers with code
Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimation
Face images contain a wide variety of attribute information.
LiGNN: Graph Neural Networks at LinkedIn
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework.
Interactive Image Synthesis with Panoptic Layout Generation
In particular, the stuff layouts can take amorphous shapes and fill up the missing regions left out by the instance layouts.
Ranked #2 on
Layout-to-Image Generation
on Visual Genome 128x128
Geometric Structure Preserving Warp for Natural Image Stitching
Further, an optimized GES energy term is presented to reasonably determine the weights of the sampling points on the geometric structure, and the term is added into the Global Similarity Prior (GSP) stitching model called GES-GSP to achieve a smooth transition between local alignment and geometric structure preservation.
iNAS: Integral NAS for Device-Aware Salient Object Detection
Existing salient object detection (SOD) models usually focus on either backbone feature extractors or saliency heads, ignoring their relations.
Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
For the segmented masks of intact lung and infected regions, the best method could achieve 0. 972 and 0. 757 measure in mean Dice similarity coefficient on our test benchmark.
Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization).
Discernible Image Compression
Experiments on benchmarks demonstrate that images compressed by using the proposed method can also be well recognized by subsequent visual recognition and detection models.
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis
The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma and cavitary types simultaneously. Then the Noisy-Or Bayesian function was used to generate an overall infection probability. Finally, a quantitative diagnostic report was exported. The results showed that the recall and precision rates, from the perspective of a single lesion region of PTB, were 85. 9% and 89. 2% respectively.
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images
In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views.
