AI BriefingVLM Training Strategies and Architecture Guide
Key point
This explains the core training strategies and architecture of Vision-Language Models, which combine visual and language information.
Details
Vision-Language Models (VLM) combine two different modalities, images and text, to perform complex tasks such as image captioning, Visual Question Answering (VQA), and zero-shot image classification.
The model largely consists of an image encoder, a text encoder, and a fusion strategy that combines the two pieces of information. Recently, the mainstream approach has been based on the Transformer architecture, learning the features of each modality either individually or jointly.
The main training strategies are as follows:
- Contrastive Learning: A method that maximizes the similarity between image and text pairs (e.g., CLIP).
- PrefixLM: A method that processes visual information through text prompts.
- Cross Attention: A method that fuses information through interaction between the two modalities.
- MLM / ITM: A method that predicts masked tokens or determines whether an image and text match.
By utilizing Hugging Face's Transformers library, you can directly experiment with and implement these latest VLM techniques.
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