WhACC, a two-stage ResNet50V2 and LightGBM classifier with engineered temporal features, matches expert human whisker-touch labeling and reduces curation effort by over 98%.
Learning to Localize Temporal Events in Large-scale Video Data
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abstract
We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset. This emerging field within video recognition can enable applications to identify the precise time a specified event occurs in a video, which has broad implications for video search. To address this we present two separate approaches: (1) a gradient boosted decision tree model on a crafted dataset and (2) a combination of deep learning models based on frame-level data, video-level data, and a localization model. The combinations of these two approaches achieved 5th place in the 3rd Youtube-8M video recognition challenge.
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WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance
WhACC, a two-stage ResNet50V2 and LightGBM classifier with engineered temporal features, matches expert human whisker-touch labeling and reduces curation effort by over 98%.