16 papers with code • 0 benchmarks • 0 datasets
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An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans
Finding an optimal correspondence between point sets is a common task in computer vision.
Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem.
This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images.
This new training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training the multiple parallel auxiliary heads supervised by one-to-many label assignments such as ATSS and Faster RCNN.
Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.
In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase.
Usual relations between entities could be captured using graphs; but those of a higher-order -- more so between two different types of entities (which we term "left" and "right") -- calls for a "bipartite hypergraph".