VLG: General Video Recognition with Web Textual Knowledge

• URL: http://arxiv.org/abs/2212.01638v1
• Date: Sat, 3 Dec 2022 15:46:49 GMT
• Title: VLG: General Video Recognition with Web Textual Knowledge
• Authors: Jintao Lin, Zhaoyang Liu, Wenhai Wang, Wayne Wu, Limin Wang
• Abstract summary: We focus on the general video recognition (GVR) problem of solving different recognition tasks within a unified framework. By leveraging semantic knowledge from noisy text descriptions crawled from the Internet, we present a unified visual-linguistic framework (VLG) Our VLG is first pre-trained on video and language datasets to learn a shared feature space, and then devises a flexible bi-modal attention head to collaborate high-level semantic concepts under different settings.
• Score: 47.3660792813967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging semantic knowledge from noisy text descriptions crawled from the Internet, we focus on the general video recognition (GVR) problem of solving different recognition tasks within a unified framework. The core contribution of this paper is twofold. First, we build a comprehensive video recognition benchmark of Kinetics-GVR, including four sub-task datasets to cover the mentioned settings. To facilitate the research of GVR, we propose to utilize external textual knowledge from the Internet and provide multi-source text descriptions for all action classes. Second, inspired by the flexibility of language representation, we present a unified visual-linguistic framework (VLG) to solve the problem of GVR by an effective two-stage training paradigm. Our VLG is first pre-trained on video and language datasets to learn a shared feature space, and then devises a flexible bi-modal attention head to collaborate high-level semantic concepts under different settings. Extensive results show that our VLG obtains the state-of-the-art performance under four settings. The superior performance demonstrates the effectiveness and generalization ability of our proposed framework. We hope our work makes a step towards the general video recognition and could serve as a baseline for future research. The code and models will be available at https://github.com/MCG-NJU/VLG.

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