arXiv (All)
open access
V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
artificial-intelligencecomputation-and-languagecomputer-vision-and-pattern-recognitionmachine-learning
Embedding-level scaling expands the capacity of language models through lookup-based memory rather than by increasing model depth, width, or dense computation. Extending this idea to vision-language models (VLMs) is challenging because visual features are continuous, spatially structured, and query-
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
- SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models#1049655
- EM^2Mem: Event-Centric Multimodal Memory for Large Language Models#819990
- AdaptInfer: Adaptive Token Pruning for Vision-Language Model Inference via Dynamical Text Guidance#1200775
- Kernel-Based Steering of CLIP with Vision-Language Model Preferences#1116534
- Multi-Image Visual Token Pruning in Large Visual Language Models#820643
Record · ID 1468623
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