KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
arXiv:2606.04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are typically compared through downstream performance, such evaluations often do not explain how their representations differ structurally. In this work, we study this problem through the task of Contrastive Embedding Clustering: identifying sample subsets that are weakly clustered under one representation but strongly clustered under another. We propose \emph{Kernel Optimization for Discrepancy Analysi
The proliferation of various vision-language foundation models necessitates better tools for understanding their underlying representation differences beyond simple performance metrics, especially as model complexity grows.
This research provides a methodology to deeply analyze and compare the internal workings of foundational AI models, which is crucial for model selection, adversarial robustness, and controlled development in multimodal AI.
The ability to structurally compare and align representation spaces of large foundation models moves beyond black-box performance evaluations, enabling more nuanced understanding and targeted improvement.
- · AI researchers and developers
- · Multimodal AI system builders
- · Model auditing and explainability platforms
- · Developers relying solely on superficial benchmark scores
- · Homogeneous model ecosystems
Improved understanding of how different multimodal foundation models perceive and process information.
More robust and tailored integration of vision-language models into diverse applications based on their specific representational strengths.
Enhanced ability to combine and fine-tune various foundation models into hybrid systems that leverage complementary representational properties.
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Read at arXiv cs.LG