VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding
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2601.05125
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de Rodrigo
de-Rodrigo
AI & ML interests
Synthetic Datasets, Multimodal LLMs, Computer Vision
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We are happy to share the VERSE Methodology paper via arXiv! 📃💫
VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding (2601.05125)
We usually train VLMs on visual synthetic data that we (as humans) label as photorealistic. We argue that this is an anthropocentric perspective imposed to a model that might not synthetize visual information as we do. VERSE helps to visualize latent space and overlay visual features to detect poor-performance regions and take action to include better-suited training sets to boost model performance.
Resources:
- Code: https://github.com/nachoDRT/VrDU-Doctor
- Hugging Face Space: de-Rodrigo/Embeddings
Want to collaborate? Do you have any feedback? 🧐
PD: As always, we are grateful to Hugging Face 🤗 for providing the fantastic tools and resources we find on the platform!
VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding (2601.05125)
We usually train VLMs on visual synthetic data that we (as humans) label as photorealistic. We argue that this is an anthropocentric perspective imposed to a model that might not synthetize visual information as we do. VERSE helps to visualize latent space and overlay visual features to detect poor-performance regions and take action to include better-suited training sets to boost model performance.
Resources:
- Code: https://github.com/nachoDRT/VrDU-Doctor
- Hugging Face Space: de-Rodrigo/Embeddings
Want to collaborate? Do you have any feedback? 🧐
PD: As always, we are grateful to Hugging Face 🤗 for providing the fantastic tools and resources we find on the platform!
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