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Detecting actions in untrimmed videos should not be limited to a small,closed set of classes. We present a simple, yet effective strategy foropen-vocabulary temporal action detection utilizing pretrained image-textco-embeddings. Despite being trained on static images rather than videos, weshow that image-text co-embeddings enable openvocabulary performancecompetitive with fully-supervised models. We show that the performance can befurther improved by ensembling the image-text features with features encodinglocal motion, like optical flow based features, or other modalities, likeaudio. In addition, we propose a more reasonable open-vocabulary evaluationsetting for the ActivityNet data set, where the category splits are based onsimilarity rather than random assignment.

Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features