Solutions to Improve the Accuracy of Video Sentiment Analysis
To solve the problem of inaccurate video sentiment analysis, it can be achieved by the following multidimensional approach:
- Data preprocessing optimization: Ensure video clarity of at least 1080p and audio sampling rate ≥ 16kHz to avoid compression distortion. It is recommended to use professional camera equipment to shoot samples
- Multimodal fusion strategy: Enable HumanOmni's video_audio mode (add the -modal video_audio parameter) to analyze both facial expressions and voice intonation, e.g.: python inference.py -modal video_audio -model_path . /HumanOmni_7B -video_path sample.mp4 -instruct "Analyze emotion considering both face and voice "
- Parameter tuning program: Adjust the -temperature parameter to a range of 0.7-1.2 to increase output diversity when emotions are complex. Add -top_k 40 and -top_p 0.9 parameters to optimize result generation.
- Feedback iteration mechanism: Fine-tune the model with a customized dataset available for error results, prepare 100+ annotated samples to run: bash scripts/train/finetune_humanomni.sh
Special Note: Insufficient ambient light will reduce the accuracy of facial recognition 37%, and it is recommended that the video be captured in an environment of more than 500lux. Multi-angle cameras can be set up for synchronized analysis of key scenes.
This answer comes from the articleHumanOmni: a multimodal macromodel for analyzing human video emotions and actionsThe
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