arXiv (All)
open access
Losing the name before the box: measuring and repairing what narrow fine-tuning costs a detector outside its deployment vocabulary
A detector pretrained on a broad corpus is fine-tuned on a narrow domain, its in-domain accuracy improves, and it ships. We ask what happens meanwhile to its coverage of objects the vocabulary never names, which in obstacle detection and inspection carry the risk. No in-domain test set holds an exam
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
- C2FXNet: Coarse-to-Fine Scene Expert for Unified Object Detection across Adverse Weather#1061328
- TestDG: Test-time Domain Generalization for Continual Test-time Adaptation#1000497
- Contextual Contamination and the Gendered Accelerant- A Controlled Pilot on Pruning, Density, and Semantic Entrapment#244941
- Detecting Is Not Resolving: The Monitoring Control Gap in Retrieval Augmented LLMs#229546
- ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation#1083065
Record · ID 1131268
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