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

• URL: http://arxiv.org/abs/2212.10596v1
• Date: Tue, 20 Dec 2022 19:12:58 GMT
• Title: Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features
• Authors: Vivek Rathod, Bryan Seybold, Sudheendra Vijayanarasimhan, Austin Myers, Xiuye Gu, Vighnesh Birodkar, David A. Ross
• Abstract summary: We present a simple, yet effective strategy for open-vocabulary temporal action detection utilizing pretrained image-text co-embeddings. We show that image-text co-embeddings enable openvocabulary performance competitive with fully-supervised models.
• Score: 9.140696309404133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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