Video Analytics Accuracy Improvement Solution
To improve video comprehension details, a combination of the following methods is recommended:
- Frame sampling optimization:
1. Use of a specialized version of OmniLive (supports dynamic frame rate adjustment)
2. Keyframe extraction interval adjusted from default 30 frames to 15-20 frames
3. Enabling motion compensation algorithms for fast motion segments - Model Configuration:
1. Load internlm-xcomposer2d5-ol-7b specialized video models
2. Set frame_analysis_level=2 at pipe initialization (fine-grained mode)
3. Enable temporal attention mechanism: temporal_attention=True - Post-processing methods:
1. Application of NLP entity enhancement techniques to the outputs
2. Cross-modal validation using models such as CLIP
3. Establishment of a thesaurus of operational keywords to support analysis
Tests show that combining motion compensation and fine-grained patterns improves action recognition accuracy by 47%
This answer comes from the articleInternLM-XComposer: a multimodal macromodel for outputting very long text and image-video comprehensionThe
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