Harvesting large-scale weakly-tagged image databases from the web

55 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Visual Features from Large Weakly Supervised Data (2015, neutral): “In contrast to our work, many prior studies also attempt to explicitly discard low-quality labels by developing algorithms that identify relevant image-hashtag pairs from a weakly labeled dataset [12, 35, 54].”
Webly Supervised Learning of Convolutional Networks (2015, background): “In this paper, we take the ﬁrst step in demonstrating that it is indeed possible to have competitive or even better performance to ImageNet pretrained CNNs by just exploiting web data at much larger scales.”
A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics (2012, background): “A task related to tag-to-image search, though one we do not consider directly, is re-ranking of contaminated image search results for the purpose of dataset collection (Berg and Forsyth, 2006; Fan et al., 2010; Frankel et al., 1997; Schroff et al., 2007).”
ConceptMap: Mining Noisy Web Data for Concept Learning (2014, background): “Learning Object Categories from Noisy Web Data: Several recent studies tackle the problem of building qualified training sets by using images returned from image search engines[17,3,1,13,33,48].”