Search Results for author:
Found 10 papers, 2 papers with code
GraphEx: A Graph-based Extraction Method for Advertiser Keyphrase Recommendation
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
Graphite: A Graph-based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation
We present Graphite, a graph-based classifier model that provides real-time keyphrase recommendations that are on par with standard text classification models.
Polarimetric PatchMatch Multi-View Stereo
PatchMatch Multi-View Stereo (PatchMatch MVS) is one of the popular MVS approaches, owing to its balanced accuracy and efficiency.
Polarimetric Multi-View Inverse Rendering
We then refine the initial model by optimizing photometric rendering errors and polarimetric errors using multi-view RGB, AoP, and DoP images, where we propose a novel polarimetric cost function that enables an effective constraint on the estimated surface normal of each vertex, while considering four possible ambiguous azimuth angles revealed from the AoP measurement.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.
Unsupervised Embedding of Hierarchical Structure in Euclidean Space
To learn the embedding, we revisit using a variational autoencoder with a Gaussian mixture prior, and we show that rescaling the latent space embedding and then applying Ward's linkage-based algorithm leads to improved results for both dendrogram purity and the Moseley-Wang cost function.
Polarimetric Multi-View Inverse Rendering
A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) of reflected light is related to an object's surface normal.
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
Melodic Phrase Segmentation By Deep Neural Networks
Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis.
