ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/hiwea/ProCAP

Terminology

• Benchmark — 20× in this paper · explained in 2 episodes
• Generalization — 17× in this paper · explained in 13 episodes
• Regularization — 17× in this paper · explained in 3 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Adaptation — 13× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Robustness — 9× in this paper · explained in 5 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Distribution Shift — 6× in this paper · explained in 5 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Embeddings — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Few-Shot Learning — 2× in this paper · explained in 4 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality
• Mixture of Prompt Learning for Vision Language Models
• MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation
• Adaptive Multi-Modality Prompt Learning
• Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models
• In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model

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