To improve the accuracy of short video content analysis, the following steps can be implemented:
- multimodal integration: Using ARC-Hunyuan-Video-7B to simultaneously process visual, audio, and textual information of videos to avoid the limitations of single modal analysis.
- Time Stamp Labeling Enhancement: Enable the model's
timestamp_captioningfunction, by means of the--task timestamp_captioningParameters are run to accurately label the time period in which the event occurs, improving key frame recognition. - Hardware Optimization: Use NVIDIA H20 and above GPUs and ensure a CUDA 12.1 environment to ensure that the model's computational resources are fully invoked.
- Data preprocessing: Keep the video within 1-5 minutes, too long content needs to be processed in segments with preprocessing scripts to avoid dilution of information density.
With the above methods, the analysis results in complex scenes (such as fast camera switching or mixing background sounds) can be significantly improved.
This answer comes from the articleARC-Hunyuan-Video-7B: An Intelligent Model for Understanding Short Video ContentThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of lack of accuracy in analyzing short video content?
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