vlm
AI, ML & GenAI interview questions tagged vlm, across every topic.
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Concepts behind "vlm"
The curriculum that explains the ideas these questions test.
Core
Multimodal Models and VLMsMultimodal models handle more than text, most commonly vision-language models (VLMs) that take images and text together. The key idea is a shared representation: a vision encoder turns an image into embeddings projected into the language model's space, so the LLM can reason over pixels and words jointly. CLIP-style contrastive training puts text and images in one embedding space, making cross-modal search possible. AI, ML, and GenAI interviews probe it because document understanding, image search, and visual agents all build on it.🧠 Foundations of LLMs & GenAISign in
Core
Multimodal Fusion ArchitecturesThere are four ways to get an image into a language model, and they differ by how late the modalities meet: dual encoders (CLIP), cross-attention resamplers (Flamingo, Q-Former), projectors that turn patches into tokens (LLaVA), and natively multimodal pretraining. Each is right for a different job, and the decision rule is short: retrieval wants a dual encoder, reasoning wants a projector or cross-attention. AI, ML, and GenAI interviews probe this because picking the wrong family means paying for reasoning capacity you cannot index, or building a search system that cannot answer questions.🧠 Foundations of LLMs & GenAISign in