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Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
[article]
2017
arXiv
pre-print
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the official test set. We attribute the good performance to three components: 1) Refined video representation learning with residual links and hypercolumns 2) Latent concept mining which captures interactions among concepts. 3) Learning with temporal segments and
arXiv:1707.01408v3
fatcat:xyc4rtvrz5e7pjg5luxuux7ddi