arXiv:2605.28809v1 Announce Type: cross Abstract: Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing similarity between visual and textual embeddings obtained from template prompts, e.g., ``a photo of a [CLASS]''. This seemingly monolithic matching process can be decomposed into two conceptually distinct stages: attribute extraction and attribute aggregation. For example, a model may recognize cat using attributes such as fur texture and whiskers. When learning a new class like car, the model
The paper addresses a critical challenge in real-world AI deployment - the ability of models to learn new categories incrementally without forgetting old ones, especially within the context of large pre-trained models like CLIP.
Improving Class-Incremental Learning (CIL) is crucial for developing robust, adaptive AI systems that can continuously learn and evolve, moving closer to artificial general intelligence and broader enterprise adoption.
This research provides a more nuanced approach to CLIP-based CIL by decomposing the classification process, potentially leading to more efficient and effective incremental learning in AI models.
- · AI researchers
- · Developers of adaptive AI systems
- · Industries relying on continuous model updates
- · AI systems requiring frequent full retraining
- · Monolithic AI development approaches
More sophisticated and flexible AI models capable of adapting to new data streams in real-time.
Reduced operational costs for AI model maintenance and deployment in dynamic environments.
Acceleration of AI applications in domains requiring constant learning, like robotics and autonomous agents, where new objects or scenarios are frequently encountered.
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Read at arXiv cs.LG