Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective
arXiv:2606.14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge. While existing approaches have adequately studied continual learning and catastrophic forgetting in vision-language models (VLMs), the theoretical understanding of modality-specific contributions across a sequence
The proliferation of advanced vision-language models makes understanding their foundational learning challenges crucial for future development, particularly continual learning and catastrophic forgetting.
Improving the theoretical understanding of complex AI systems like VLMs is essential for building more stable, adaptable, and reliable AI, which directly impacts their applicability across industries.
This theoretical work provides a deeper insight into the mechanisms of cross-modal contributions in continual learning, potentially leading to more robust VLM architectures that minimize catastrophic forgetting.
- · AI researchers
- · Generative AI developers
- · Multimodal AI applications
- · Machine learning theory
- · Current VLM architectures prone to catastrophic forgetting
Improved theoretical understanding of vision-language models' continual learning capabilities.
Development of more stable and efficient multimodal AI systems for deployment in dynamic environments.
Accelerated progress in AI agent development that can learn and adapt continuously without losing prior knowledge.
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Read at arXiv cs.LG