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This paper introduces fsLDA, a fast variational inference method for supervised LDA, which overcomes the computational limitations of the original supervised LDA and enables its application in large-scale video datasets. In addition to its scalability, our method also overcomes the drawbacks of standard, unsupervised LDA for video, including its focus on dominant but often irrelevant video information (e.g. background, camera motion). As a result, experiments in the UCF11 and UCF101 datasetsdoi:10.1145/2964284.2967237 dblp:conf/mm/KatharopoulosPD16 fatcat:ee5ob32umbhvzg67ttm2af3yaa