21 skills found
microsoft / OpenKPAutomatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
MLearing / Keras Brats Improved Unet3d3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
juanb09111 / FinnForestThis paper introduces a forest dataset called FinnWoodlands, which consists of RGB stereo images, point clouds, and sparse depth maps, as well as ground truth manual annotations for semantic, instance, and panoptic segmentation
Hua-YS / Semantic Segmentation With Sparse Labelscodes and data for learning from sparse annotations
lizhaoliu-Lec / CPCMThis is the official repo for Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation (ICCV 23).
fontforge / LibuninameslistA library with a large (sparse) array mapping each unicode code point to the annotation data for it provided in http://www.unicode.org/Public/UNIDATA/NamesList.txt
Younger330 / SemanticEnergyLossBIBM2023 regular paper for "Addressing Sparse Annotation: a Novel Semantic Energy Loss for Tumor Cell Detection from Histopathologic Images"
links-ads / Igarss SpadaLand Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
ShuaiyiHuang / SCorrSANOfficial Code for ECCV2022: Learning Semantic Correspondence with Sparse Annotations
mrcfps / WESUPSource code for *Weakly Supervised Histopathology Image Segmentation with Sparse Point Annotations*.
JordanMakesMaps / Fast Multilevel Superpixel SegmentationAllows users to convert sparse point annotations into dense annotations both quickly and automatically.
Listening-Lab / AnnotatorListening Lab audio analysis and annotation tool. Develop audio classification models to detect sparse audio events within field recordings
Shathe / CoralSeg Learning Coral Segmentation From Sparse AnnotationsCoralSeg: Learning coral segmentation from sparse annotations
ZhangYongshan / MSFSTMM2020: Multi-view multi-label learning with sparse feature selection for image annotation
yuqcheng / ScBalanceA scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data
panzhiyi / AADNetPoint Cloud Semantic Segmentation with Sparse and Inhomogeneous Annotations, AAAI 2025
Solar-MC / SLAPSparse Lunar Annotation Predicate (Lunar Mappings)
Hansong-Zhang / CCCAAAI 2024, Coupled Confusion Correction: Learning from Crowds with Sparse Annotations
ratschlab / Genome Graph AnnotationSparse Binary Relation Representations for Genome Graph Annotation
MathSZhang / CALLRCALLR is a semi-supervised cell type annotation method for scRNA-seq data. It requires a small number of cell type labels, and annotate the remaining cells by combining clustering and sparse logistic regression.
