arXiv (OAI Expanded)
open access
TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models
Prompt learning adapts vision-language models, such as CLIP, by adjusting a small set of context tokens. However, under few-shot supervision, even moderate label noise can disrupt prompt optimization. To address this issue, we propose TecoPrompt, a closed-loop robust prompt-learning framework that r
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
- Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases#987206
- Robustifying Vision-Language Models via Test-Time Prompt Adaptation#361448
- Prompt-Space Meta-Learning Does Not Transfer Across Users: A Frozen-LLM Negative Result#822056
- The Mirage of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models#1012638
- VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models#615039
Record · ID 1122850
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