---

name: modality_feature_fusion description: Fuse paired infrared and visible arrays while preserving thermal salience and visible texture through controllable local/global weights.

Modality Feature Fusion

When To Use

Use this skill for bounded infrared-visible image-fusion analysis derived from arXiv:2206.09581, especially when preserving modality-specific features, comparing explicit/implicit mechanisms, or running proxy recovery.

Inputs

See the corresponding module document in the attempt directory. Inputs must be local text or numeric arrays; do not read any original paper repository.

Outputs

The skill returns deterministic JSON-compatible diagnostics suitable for downstream recovery and analysis.

Workflow

1. Validate input shapes or text content.
2. Execute the module-specific mechanism with deterministic defaults.
3. Return compact evidence and numeric diagnostics.
4. For recovery, keep proxy/reduced labels explicit.

Validation

Run python /share/project/yuyang/workspace/Paper2Skills/Paper2Skills-Agent/src/packages/paper2skills-agent/src/paper2skills/skills/module-to-skill/scripts/validate_skill_tree.py /share/project/yuyang/workspace/Paperbench/record/case13/extracted_skills_attempt_001/exorl_unsupervised_rl/modality_feature_fusion --run-tests.

Limitations

This skill implements a lightweight, reusable proxy for the paper mechanism, not the full 21-test-set benchmark from the paper.