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Continual Multimodal Knowledge Graph Construction
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Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-world dynamism of continuously emerging entities and relations, often succumbing to catastrophic forgetting-loss of previously acquired knowledge. This study introduces benchmarks aimed at fostering the development of the continual MKGC domain. We further introduce MSPT framework, designed to surmount the shortcomings of existing MKGC approaches during multimedia data processing. MSPT harmonizes the retention of learned knowledge (stability) and the integration of new data (plasticity), outperforming current continual learning and multimodal methods. Our results confirm MSPT's superior performance in evolving knowledge environments, showcasing its capacity to navigate balance between stability and plasticity.
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Cited by 2 Pith papers
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Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
The paper offers a comprehensive survey and proposes a new taxonomy for continual learning strategies in VLMs and MLLMs to combat catastrophic forgetting beyond traditional methods.
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GNSP: Gradient Null Space Projection for Preserving Cross-Modal Alignment in VLMs Continual Learning
GNSP combines null-space gradient projection with contrastive distillation and a modality alignment loss to fine-tune CLIP continually while preserving its zero-shot transfer and retrieval.
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