3260 papers • 126 benchmarks • 313 datasets
Long-video Activity Recognition (LAR) focuses on modeling long-term relations among all actions in a long video. LAR aims to recognize all actions within each long video, under the weak supervision of the video-level action category set. The mean average precision metric (mAP) is used for evaluation.
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Timeception achieves impressive accuracy in recognizing the human activities of Charades, Breakfast Actions and MultiTHUMOS, and it is demonstrated that Timeception learns long-range temporal dependencies and tolerate temporal extents of complex actions.
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