arXiv (All)
open access
Multimodal Representation Learning Conditioned on Semantic Relations
Multimodal representation learning has been largely driven by contrastive models such as CLIP, which learn a shared embedding space by aligning paired image-text samples. While effective for general-purpose representation learning, such models typically produce a single embedding per sample that is
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
- What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies#1325931
- Refining the multimodality of semantic representations#155929
- ITO: Multi-View Alignment and Training-Time Fusion for Image-Text Pretraining#1143698
- Natural Language Generation from Visual Events: State-of-the-Art and Key Open Questions#1326092
- ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression#1141269
Record · ID 1186349
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