ClipCap, short for CLIP Prefix for Image Captioning, is a novel approach to image captioning that leverages the power of CLIP encoding and a language model like GPT2.
Here are four key features of ClipCap
- CLIP Encoding: ClipCap uses CLIP encoding as a prefix to the caption. The CLIP model contains rich semantic features trained with textual context, making it ideal for vision-language perception.
- Language Model Fine-tuning: After using CLIP encoding as a prefix, ClipCap fine-tunes a language model to generate the image captions. This approach allows for a wide understanding of both visual and textual data.
- Efficient Training: ClipCap requires only quick training to produce a competent captioning model. It can generate meaningful captions for large-scale and diverse datasets without additional annotations or pre-training.
- Lighter Architecture: The method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen. This results in a lighter architecture with fewer trainable parameters. Despite its simplicity, ClipCap achieves comparable results to state-of-the-art methods on challenging datasets.