LSCOM Lexicon Definitions and Annotations (Version 1.0)

By L. Kennedy, Alexander G. Hauptmann. 2006

112 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks

How researchers see this paper

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

In Evaluation & Benchmarks

1. Trevor Darrell
2. Xiansheng Hua
3. Oriol Vinyals
4. Damian Borth
5. Tao Chen
6. Ning Zhang
7. Guo-Jun Qi
8. Eric Tzeng

Citations in the corpus

• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013, methodology): “We investigate the “supervised pre-training” approach proven successful in computer vision and multimedia settings using a concept-bank paradigm (Kennedy & Hauptmann, 2006; Li et al., 2010; Torresani et al., 2010) by learning the features on large-scale data in a supervised setting, then…”
• DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks (2014, methodology): “Supervised pretraining approach using a concept-bank paradigm [17, 33] is also proven successful in computer vision and multimedia settings.”
• Unified Video Annotation via Multigraph Learning (2009, methodology): “…more efficient compared with typical existing methods such as support vector machine (SVM), and this advantage is particularly encouraging when annotating a large lexicon of concepts, such as the large scale concept ontology for multimedia (LSCOM) that includes hundreds of concepts [23].”
• Learning Semantic Image Representations at a Large Scale (2014, methodology): “We investigate the “supervised pre-training” approach proven successful in computer vision and multimedia settings using a concept-bank paradigm [61, 75, 111] by learning the features on large-scale”