arXiv (All)
open access
On the Geometry of Multimodal Saturation: Riemannian VICReg
In self-supervised learning, a third modality should improve, or at least preserve, performance. Across nine image-text-tabular datasets, we show that it instead harms performance: the trimodal model underperforms its own best bimodal subset in 55.6% of paired runs under VICReg. The same failure occ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hierarchical Barycentric Multimodal Representation Learning for Medical Image Analysis#157804
- Hierarchical Barycentric Multimodal Representation Learning for Medical Image Analysis#154940
- Beyond Forced Modality Balance: Intrinsic Information Budgets for Multimodal Learning#1342430
- Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining#1232756
- 0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation#1061055
Record · ID 1202515
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive