Learning to Prompt With Refining Text Knowledge for Zero-Shot Video Action Recognition

Foundational vision-language models (VLMs) like CLIP are redefining the vision domain with their exceptional generalization capabilities. Prompt-based learning methods adapt pre-trained VLMs to video action recognition tasks using task-specific learnable text tokens. However, these tokens often struggle to generalize to unseen categories, as they tend to forget general textual knowledge. To address this, we construct knowledge prompts composed of handcrafted and descriptive prompts and introduce a novel knowledge-guided context mapping to enhance the generalization of learnable prompts to unseen categories. This approach mitigates the forgetting of fundamental knowledge by reducing the discrepancy between learnable prompts and knowledge prompts while simultaneously allowing the prompts to extract rich contextual knowledge from LLM data. Then, incorporating the knowledge-guided context mapping into the contrastive loss enables zero-shot transfer of prompts to new categories and data, providing discriminative prompts for both seen and unseen tasks. In addition, we propose an advanced temporal aggregation method that refines uniform mean pooling by incorporating frame-level textual relevance scoring. Extensive evaluations on multiple benchmarks demonstrate that learning to prompt with refining text knowledge is an effective quick-tuning method, achieving superior sample generalization performance without increasing training parameters.