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Found 4 papers, 1 papers with code
Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling
Human feedback on the pairwise similarity can be used to improve the clustering, but existing approaches do not guarantee accurate count estimates.
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling
Many modern applications use computer vision to detect and count objects in massive image collections.
Domain Adaptor Networks for Hyperspectral Image Recognition
We consider the problem of adapting a network trained on three-channel color images to a hyperspectral domain with a large number of channels.
StarcNet: Machine Learning for Star Cluster Identification
We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic Ultraviolet Survey).
