arXiv (All)
open access
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors
Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends critically on the examples selected. Recent counterfactual selection methods improve ICL by constructing demonstrations that expose how individual at
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
- Verb-ICL: Rethinking In-Context Learning for Structured Prediction#1200376
- When to Rethink: Learning Multi-Perspective Self-Verification for Vision-Language Models#1216100
- Long-Context Demonstration Selection Using State Space Models#1011603
- Capturing In-Context Learning Dynamics with Task Operators#1155989
- TraceICL: Thread-Resolved Demonstration Curation with Consistency Calibration for Robust Multimodal In-Context Learning#1296523
Record · ID 1341858
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