arXiv (All)
open access
CrowdCue: Specialist-Cue Conditioning for Vision-Language Crowd Counting
Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene, yet their raw counting accuracy sits in the range of sub-million-parameter specialist regressors. The open question is whether auxiliary guidance f
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
- VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models#179175
- Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting#614955
- Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting#613988
- From Knowing to Abstaining: Bridging the Representation-Action Gap in Vision-Language Models#1116984
- The Mirage of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models#1012638
Record · ID 1048407
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