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arXiv · 2212.10596

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

Vivek Rathod·Bryan Seybold·Sudheendra Vijayanarasimhan·Austin Myers·Xiuye Gu·Vighnesh Birodkar·David A. Ross
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Abstract

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

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Vivek Rathod, Bryan Seybold, Sudheendra Vijayanarasimhan, Austin Myers, Xiuye Gu, Vighnesh Birodkar, David A. Ross. 2023-01-10. Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features. https://arxiv.org/abs/2212.10596

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