ConceptMap: Mining Noisy Web Data for Concept Learning

25 citations recorded by the source corpus.

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Webly Supervised Learning of Convolutional Networks (2015, background): “So, what we want is an approach that can learn visual representation from Flickr-like images.”
Automatic Construction of Action Datasets Using Web Videos with Density-Based Cluster Analysis and Outlier Detection (2015, background): “Therefore, rather than hard clustering data into a specific number of subsets as some approaches which also aim to deal with intraclass variations in concepts [23, 17], we use hierarchy clustering which allows different clusters to share the same instances.”
Sense discovery via co-clustering on images and text (2015, background): “There have been early efforts on automatically training visual classifiers using web data to build large datasets automatically [26, 39, 42, 27, 20], find iconic images [5, 35] and improve image retrieval results [19, 28, 45, 34].”
Webly Supervised Concept Expansion for General Purpose Vision Models (2022, methodology): “As web results improved, works used this data to build object detectors [11, 15, 46, 54, 71, 83], attribute detectors [21], image taggers [81], large vocabulary categorization models [24,56,85] and finegrained recognition models [39, 57], segmentation models [33, 70, 73], online dataset builders [45”