Vision encoders

So wird trainiert

Supervised labels, masked-image modeling, self-distillation, reconstruction, and image–text contrastive objectives produce different visual invariances and levels of spatial detail.

So läuft die Inferenz

The encoder runs once per image or frame batch. Pooled outputs favor retrieval and classification; dense token maps preserve more localization detail for detection or multimodal reasoning.

Stärken

• •Reusable visual features
• •Efficient classification, retrieval, and perception
• •Can connect images to language models through a projector or cross-attention

Zielkonflikte

• •Resolution and patch size determine lost detail and token cost
• •Training objectives create different blind spots
• •A pooled vector is insufficient for many spatial tasks

Geeignet, wenn

• The system must understand or retrieve visual content
• You can choose pooled versus spatial features based on the task
• Small text, charts, and domain imagery are evaluated explicitly

Vermeiden oder hinterfragen, wenn

• Visual generation is mistakenly expected from the encoder alone
• Input resolution removes required details
• A generic image benchmark substitutes for the actual domain