Multimodal Task Accuracy Improvement Program
Optimization strategies for image understanding tasks include:
- preprocessing enhancement: in
preprocessors/vision.pymid-range adjustmentaugmentation_levelParametric enhancement of input quality - model fusion: Combined CLIP and BLIP models, modified
multimodal_strategyfor ensemble - Post-processing calibration: Enable
--post_verifyParameters allow textual intelligences to secondarily calibrate visual outputs - domain adaptation: Use
finetune_vision.shScripts fine-tune models on specialized domain data
The test data show that using the model fusion + post-processing calibration scheme improves the accuracy from 68% to 82% in the medical image description task. it is recommended to create dedicated preset configurations for different domains.
This answer comes from the articleJoyAgent-JDGenie: an open source multi-intelligence framework to support automated processing of complex tasksThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of image description generation tasks in multimodal scenarios?
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