Representation Calibration and Uncertainty Guidance for Class-Incremental Learning based on Vision Language Model
Published:Dec 10, 2025 09:09
•1 min read
•ArXiv
Analysis
This article focuses on class-incremental learning, a challenging area in AI. It explores how to improve this learning paradigm using vision-language models. The core of the research likely involves techniques to calibrate representations and guide the learning process based on uncertainty. The use of vision-language models suggests an attempt to leverage the rich semantic understanding capabilities of these models.
Key Takeaways
Reference
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