Visual Information Integrity Assurance Program
MM-EUREKA prevents the omission of information through two mechanisms:
- Explicit visual review technique
- Activation method: add when running the script
--enable_reflectionparameters - Principle of implementation: model staged processing of images
- Phase 1: Global Feature Extraction
- Phase 2: Focusing on Key Areas (Visualized through Attention Heat Maps)
- Activation method: add when running the script
- Developer Aids
- utilization
test_reflection.pyScript Checking Model Concerns - Analyze the output of the
attention_weights.csvfile
- utilization
Enhancement measures::
- Adding text annotations to important images (modifying the JSONL
caption(Fields) - Enhancement of negative samples during training (e.g., images that intentionally obscure key areas)
- Integrated target detector pre-marks key objects in the image
typical application: In medical image analysis, the solution improves lesion identification accuracy by 15%.
This answer comes from the articleMM-EUREKA: A Multimodal Reinforcement Learning Tool for Exploring Visual ReasoningThe
May not be reproduced without permission:AI productivity tools " How to avoid critical information omission in multimodal models when processing complex images?
