SnapCap: Efficient Snapshot Compressive Video Captioning

• URL: http://arxiv.org/abs/2401.04903v1
• Date: Wed, 10 Jan 2024 03:11:21 GMT
• Title: SnapCap: Efficient Snapshot Compressive Video Captioning
• Authors: Jianqiao Sun, Yudi Su, Hao Zhang, Ziheng Cheng, Zequn Zeng, Zhengjue Wang, Bo Chen, Xin Yuan
• Abstract summary: Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. In this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera. To better extract language-related visual representations from the compressed measurement, we propose to distill the knowledge from videos via a pre-trained CLIP.
• Score: 18.016261978231835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Captioning (VC) is a challenging multi-modal task since it requires describing the scene in language by understanding various and complex videos. For machines, the traditional VC follows the "imaging-compression-decoding-and-then-captioning" pipeline, where compression is pivot for storage and transmission. However, in such a pipeline, some potential shortcomings are inevitable, i.e., information redundancy resulting in low efficiency and information loss during the sampling process for captioning. To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap. To be more specific, benefiting from the signal simulation, we have access to obtain abundant measurement-video-annotation data pairs for our model. Besides, to better extract language-related visual representations from the compressed measurement, we propose to distill the knowledge from videos via a pre-trained CLIP with plentiful language-vision associations to guide the learning of our SnapCap. To demonstrate the effectiveness of SnapCap, we conduct experiments on two widely-used VC datasets. Both the qualitative and quantitative results verify the superiority of our pipeline over conventional VC pipelines. In particular, compared to the "caption-after-reconstruction" methods, our SnapCap can run at least 3$\times$ faster, and achieve better caption results.

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