Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models
arXiv:2607.02909v1 Announce Type: cross Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language. Despite their impressive capabilities, large multimodal models (LMMs) often lack taxonomic knowledge, leading to low hierarchical visual recognition (HVR) consistency. These models typically only rely on language modeling objectives during fine-tuning and lack explicit taxonomy-aware regularization. To address this, we propose Hierarchical Representation Regularization ($HiR^2$), a simple plug-and-play regul
This research is emerging now as large multimodal models become increasingly powerful, highlighting their current limitations in understanding nuanced hierarchical relationships.
Improved taxonomic understanding in LMMs will lead to more robust, consistent, and context-aware AI systems, enhancing their practical applicability across various domains.
LMMs will be able to interpret and act upon visual and linguistic information with a deeper understanding of underlying semantic structures, moving beyond mere pattern recognition.
- · AI developers
- · Computer vision applications
- · Natural language processing
- · Data scientists
- · Models lacking hierarchical reasoning
- · Systems relying on shallow feature extraction
LMMs will exhibit better performance in complex tasks requiring hierarchical reasoning and fine-grained classification.
This could accelerate the development of more advanced AI agents capable of understanding and navigating real-world complexities.
Enhanced interpretability and reliability of AI systems could broaden their adoption in sensitive sectors like healthcare and autonomous systems.
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Read at arXiv cs.AI