arXiv (All)
open access
Using OCR Heads to Verbalize Image Semantics
How do VLMs map from pixels to semantics? To understand this general question, we focus on a narrow one: studying how VLMs perform optical character recognition (OCR). Across four models, we identify attention heads causally necessary for OCR, and discover that these are in fact general-purpose head
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
- Using OCR Heads to Verbalize Image Semantics#965456
- SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning#1161057
- SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning#1157310
- Absence is Presence: Understanding Visual Scene Negative Events Under Safety Cognitive Constraint#1014396
- Grouping by semantic and color similarity in visual working memory: An attentional mechanism, not compression mechanism.#121927
Record · ID 1012983
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