The paper "ViLT-CLIP: Video and Language Tuning CLIP with Multimodal Prompt Learning and Scenario-Guided Optimization" addresses the challenge of adapting pre-trained vision-language (V-L) models, such as CLIP, to video tasks. The authors propose a method called ViLT-CLIP, which combines multimodal prompt learning and scenario-guided optimization to improve the performance of CLIP on video recognition and video text retrieval tasks. Key contributions of the paper include: 1. **Multimodal Prompt Learning**: The method introduces learnable prompts for both the visual and language branches of CLIP, allowing the model to adapt to video-specific tasks while maintaining generalization capabilities. 2. **Scenario-Guided Optimization**: This approach ensures that the learned prompts align with the context of video scenarios, reducing the forgetting of essential knowledge in the textual branch. 3. **Balanced Performance**: ViLT-CLIP achieves competitive performance in fully supervised, zero-shot, few-shot, and base-to-novel generalization settings. The paper evaluates ViLT-CLIP on various datasets, including Kinetics-400, HMDB-51, UCF-101, and SSv2, demonstrating superior performance compared to existing methods. The results show that ViLT-CLIP effectively bridges the domain gap between image-based and video tasks, achieving better zero-shot and few-shot generalization while maintaining strong fully supervised performance.The paper "ViLT-CLIP: Video and Language Tuning CLIP with Multimodal Prompt Learning and Scenario-Guided Optimization" addresses the challenge of adapting pre-trained vision-language (V-L) models, such as CLIP, to video tasks. The authors propose a method called ViLT-CLIP, which combines multimodal prompt learning and scenario-guided optimization to improve the performance of CLIP on video recognition and video text retrieval tasks. Key contributions of the paper include: 1. **Multimodal Prompt Learning**: The method introduces learnable prompts for both the visual and language branches of CLIP, allowing the model to adapt to video-specific tasks while maintaining generalization capabilities. 2. **Scenario-Guided Optimization**: This approach ensures that the learned prompts align with the context of video scenarios, reducing the forgetting of essential knowledge in the textual branch. 3. **Balanced Performance**: ViLT-CLIP achieves competitive performance in fully supervised, zero-shot, few-shot, and base-to-novel generalization settings. The paper evaluates ViLT-CLIP on various datasets, including Kinetics-400, HMDB-51, UCF-101, and SSv2, demonstrating superior performance compared to existing methods. The results show that ViLT-CLIP effectively bridges the domain gap between image-based and video tasks, achieving better zero-shot and few-shot generalization while maintaining strong fully supervised performance.