LVLM Improves Alignment of Task-Specific Vision Models
Published:Dec 26, 2025 11:11
•1 min read
•ArXiv
Analysis
This paper addresses a critical problem in deploying task-specific vision models: their tendency to rely on spurious correlations and exhibit brittle behavior. The proposed LVLM-VA method offers a practical solution by leveraging the generalization capabilities of LVLMs to align these models with human domain knowledge. This is particularly important in high-stakes domains where model interpretability and robustness are paramount. The bidirectional interface allows for effective interaction between domain experts and the model, leading to improved alignment and reduced reliance on biases.
Key Takeaways
- •Addresses the problem of spurious correlations in task-specific vision models.
- •Proposes LVLM-VA, a method to align models with human domain knowledge.
- •Utilizes a bidirectional interface for interaction between experts and the model.
- •Demonstrates improved alignment and reduced bias on both synthetic and real-world datasets.
Reference
“The LVLM-Aided Visual Alignment (LVLM-VA) method provides a bidirectional interface that translates model behavior into natural language and maps human class-level specifications to image-level critiques, enabling effective interaction between domain experts and the model.”