Action Recognition from Single Timestamp Supervision in Untrimmed Videos

Davide Moltisanti, Sanja Fidler, Dima Damen

Research output: Contribution to conferenceConference Paperpeer-review

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Recognising actions in videos relies on labelled supervision during training, typically the start and end times of each action instance. This supervision is not only subjective, but also expensive to acquire. Weak video-level supervision has been successfully exploited for recognition in untrimmed videos, however it is challenged when the number of different actions in training videos increases. We propose a method that is supervised by single timestamps
located around each action instance, in untrimmed videos. We replace expensive action bounds with sampling distributions
initialised from these timestamps. We then use the classifier’s response to iteratively update the sampling distributions. We demonstrate that these distributions converge to the location and extent of discriminative action segments. We evaluate our method on three datasets for finegrained recognition, with increasing number of different actions per video, and show that single timestamps offer a reasonable compromise between recognition performance and labelling effort, performing comparably to full temporal supervision. Our update method improves top-1 test accuracy by up to 5.4%. across the evaluated datasets.
Original languageEnglish
Publication statusPublished - 20 Jun 2019
EventIEEE/CVF Computer Vision and Pattern Recognition, 2019 - Long Beach, California, United States
Duration: 16 Jun 201920 Jun 2019


ConferenceIEEE/CVF Computer Vision and Pattern Recognition, 2019
Abbreviated titleCVPR2019
Country/TerritoryUnited States
CityLong Beach, California
Internet address


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