VisRAG Solutions
UltraRAG's VisRAG module specializes in solving multimodal retrieval challenges:
- Jointly embedded space: Towards a unified visual-textual feature representation using the CLIP-like model
- cross-modal alignment: An adaptive alignment algorithm based on contrast learning to automatically learn intermodal associations
- Hybrid Indexing Strategy: Simultaneous support for hybrid searches of FAISS image indexes and text inverted indexes
Implementation steps
- Selecting the "VisRAG" solution in the WebUI
- Upload image datasets and corresponding text descriptions (auto-matching supported)
- Setting cross-modal training parameters ("AutoMode" is recommended for beginners)
- The system is generated after initiating training:
- Visual search demo interface
- Cross-modal similarity matrix
- Heat map analysis of key features
Performance Tuning Tips
For professional users: the weight of different modalities can be balanced by adjusting the "Modal Fusion Coefficient" (between 0 and 1), the higher the value, the stronger the influence of visual features.
This answer comes from the articleUltraRAG: A One-Stop RAG System Solution to Simplify Data Construction and Model Fine-TuningThe
May not be reproduced without permission:AI productivity tools " How to achieve efficient visual-textual cross-modal retrieval in multimodal scenarios?
