多模态 / NAVIN RESEARCH
Build a Reliable Image Understanding Pipeline
Prepare images, preserve provenance, structure outputs, and verify visual claims before using multimodal results.
Prepare images, preserve provenance, structure outputs, and verify visual claims before using multimodal results.
简要回答: Use the original or an appropriate-resolution image, provide the task and expected output schema, and preserve image identifiers. Verify high-impact visual claims with deterministic processing, a second pass, or human review because confident descriptions can still be wrong.
实施步骤
- Normalize orientation, format, and resolution without discarding needed detail.
- Send only task-relevant images and label each one clearly.
- Request structured fields with an explicit unknown option.
- Store source coordinates or image IDs with extracted claims.
验证清单
- Test small text, occlusion, unusual aspect ratios, and blank images.
- Measure field-level accuracy rather than prose similarity.
- Check accessibility and consent for uploaded images.
常见错误
- Asking one vague question about a contact sheet.
- Treating OCR-like output as exact.
- Removing provenance after extraction.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
