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Learning to Localize Temporal Events in Large-scale Video Data

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arxiv 1910.11631 v1 pith:VLUPD2YQ submitted 2019-10-25 cs.CV

Mikel Bober-Irizar , Miha Skalic , David Austin This is my paper · ORCID
classification cs.CV
keywords videodataaddressapproachesdataseteventslarge-scalelearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance

    cs.CV 2025-01 conditional novelty 5.0 of 10

    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%.

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