Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

arXiv:2610.01180 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Accuracy — 21× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• In-Context Learning — 8× in this paper · explained in 4 episodes
• Skeleton-and-Strategy Prompting — 6× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Advances in Language and Vision Research — 1× in this paper
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP
• GPT-4o System Card
• An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
• VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning

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