AdaBoosting Text Prompts for Vision-Language Models

• URL: http://arxiv.org/abs/2607.00684v1
• Date: Wed, 01 Jul 2026 09:28:55 GMT
• Title: AdaBoosting Text Prompts for Vision-Language Models
• Abstract summary: Text Prompt Boosting (TPB) treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble.<n>TPB preserves task-intrinsic, model-agnostic cues in text space, enabling robust cross-model transfer.
• Score: 23.676800230947975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Language Model (LLM)-generated descriptions not only make predictions more interpretable, but also enable reuse of the same prompts across heterogeneous VLMs. Recent works construct task-adapted text prompts with a small number of labeled images. However, existing few-shot text prompting methods do not explicitly focus on misclassified examples during prompt construction, leading to only marginal improvements even as more shots become available. To fully exploit few-shot supervision, we propose Text Prompt Boosting (TPB), an AdaBoost-inspired framework that treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble by explicitly targeting hard, misclassified examples. Extensive experiments show that TPB preserves task-intrinsic, model-agnostic cues in text space, enabling robust cross-model transfer. Across eleven classification benchmarks, TPB improves accuracy on the source model and preserves shot-driven gains when transferred to larger, more capable VLMs, where existing methods struggle to sustain such improvements.

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