CLIP Level

CLIP-level processing in computer vision focuses on leveraging the pre-trained CLIP model's powerful image-text embeddings for various tasks beyond simple image classification. Current research emphasizes efficient and effective ways to utilize CLIP features, including adapting its architecture for video understanding (e.g., through temporal modeling and clip-aware aggregation) and integrating it with other models like diffusion models and transformers for tasks such as segmentation, object tracking, and facial expression recognition. This approach is significant because it allows for zero-shot or few-shot capabilities in diverse applications, reducing the need for extensive training data and improving the generalizability of visual recognition systems.