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Found 5 papers, 2 papers with code
Detecting Deficient Coverage in Colonoscopies
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
LaSO: Label-Set Operations networks for multi-label few-shot learning
We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning.
RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
In-situ multi-scattering tomography
We formulate tomography that handles arbitrary orders of scattering, using a monte-carlo model.
Airborne Three-Dimensional Cloud Tomography
We seek to sense the three dimensional (3D) volumetric distribution of scatterers in a heterogenous medium.
