MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
–Neural Information Processing Systems
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information, as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images.
Neural Information Processing Systems
Jun-18-2026, 05:01:49 GMT
- Genre:
- Workflow (0.92)
- Research Report
- New Finding (1.00)
- Experimental Study (1.00)
- Industry:
- Education (0.92)
- Information Technology > Security & Privacy (0.67)
- Technology:
- Information Technology
- Sensing and Signal Processing > Image Processing (1.00)
- Information Management (1.00)
- Data Science > Data Mining (1.00)
- Communications > Social Media (0.67)
- Artificial Intelligence
- Vision (1.00)
- Representation & Reasoning (1.00)
- Natural Language
- Large Language Model (1.00)
- Chatbot (0.93)
- Information Retrieval (0.92)
- Machine Learning
- Neural Networks > Deep Learning (0.67)
- Performance Analysis > Accuracy (0.45)
- Information Technology