Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding

• URL: http://arxiv.org/abs/2311.15075v1
• Date: Sat, 25 Nov 2023 17:01:38 GMT
• Title: Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding
• Authors: Ruyang Liu and Jingjia Huang and Wei Gao and Thomas H. Li and Ge Li
• Abstract summary: We propose Spatial-Temporal Auxiliary Network with Mutual-guided alignment module (Mug-STAN) to extend image-text model to diverse video tasks and video-text data. Mug-STAN significantly improves adaptation of language-image pretrained models such as CLIP and CoCa at both video-text post-pretraining and finetuning stages.
• Score: 47.97650346560239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale image-language pretrained models, e.g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data. Despite the impressive performance of image-language models on various image tasks, how to effectively expand them on general video understanding remains an area of ongoing exploration. In this paper, we investigate the image-to-video transferring from the perspective of the model and the data, unveiling two key obstacles impeding the adaptation of image-language models: non-generalizable temporal modeling and partially misaligned video-text data. To address these challenges, we propose Spatial-Temporal Auxiliary Network with Mutual-guided alignment module (Mug-STAN), a simple yet effective framework extending image-text model to diverse video tasks and video-text data.Specifically, STAN adopts a branch structure with decomposed spatial-temporal modules to enable generalizable temporal modeling, while Mug suppresses misalignment by introducing token-wise feature aggregation of either modality from the other. Extensive experimental results verify Mug-STAN significantly improves adaptation of language-image pretrained models such as CLIP and CoCa at both video-text post-pretraining and finetuning stages. With our solution, state-of-the-art zero-shot and finetuning results on various downstream datasets, including MSR-VTT, DiDeMo, LSMDC, Kinetics-400, Something-Something-2, HMDB-51, UCF- 101, and AVA, are achieved. Moreover, by integrating pretrained Mug-STAN with the emerging multimodal dialogue model, we can realize zero-shot video chatting. Codes are available at https://github.com/farewellthree/STAN

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