Hierarchical Prompt Learning for Hyperbolic Vision-Language Models

arXiv:2609.24276 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Accuracy — 43× in this paper · explained in 1 episode
• Embeddings — 33× in this paper · explained in 2 episodes
• Generalization — 27× in this paper · explained in 13 episodes
• Regularization — 16× in this paper · explained in 3 episodes
• Adaptation — 11× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Aperture — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Hierarchical Classification — 2× in this paper · explained in 2 episodes
• Lowest Common Ancestor — 2× in this paper
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Tree-Induced Error — 2× in this paper
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structured Prompting — 1× in this paper · explained in 2 episodes

Sources

• IPO: Interpretable Prompt Optimization for Vision-Language Models

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