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Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 3 months ago
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      README.md

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README.md

@ -72,6 +72,8 @@ An image captioning operator takes a [towhee image](link/to/towhee/image/api/doc
# More Resources
- [CLIP Object Detection: Merging AI Vision with Language Understanding - Zilliz blog](https://zilliz.com/learn/CLIP-object-detection-merge-AI-vision-with-language-understanding): CLIP Object Detection combines CLIP's text-image understanding with object detection tasks, allowing CLIP to locate and identify objects in images using texts.
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- [Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning - Zilliz blog](https://zilliz.com/learn/exploring-openai-clip-the-future-of-multimodal-ai-learning): Multimodal AI learning can get input and understand information from various modalities like text, images, and audio together, leading to a deeper understanding of the world. Learn more about OpenAI's CLIP (Contrastive Language-Image Pre-training), a popular multimodal model for text and image data.
- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
- [From Text to Image: Fundamentals of CLIP - Zilliz blog](https://zilliz.com/blog/fundamentals-of-clip): Search algorithms rely on semantic similarity to retrieve the most relevant results. With the CLIP model, the semantics of texts and images can be connected in a high-dimensional vector space. Read this simple introduction to see how CLIP can help you build a powerful text-to-image service.
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