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Yapay ZekâÖnerilenAIML-MM-003

Vision-Language Models

Connecting a vision encoder to a language model so it can answer questions about images.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
3 / 5
Önem
4 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can describe how visual features are projected into a language model's input space and name two characteristic failure modes such as object hallucination.

Bu konuya giden rota

Rota AIML-MM-003
What Is a Model? Tasks: Regression, Classification, and Beyond Features and Targets Learning Paradigms: Where the Supervision Signal Comes From Classification Framing Linear Regression I: Prediction and Residuals Embeddings Tokens and Tokenization Images as Tensors Scores and Logits Self-Supervised Learning Loss and Fitting: Squared Error Similarity in Vector Space Multimodal Representations Multiclass Scores and Softmax Autoregression Objective Functions and Empirical Risk Query, Key, Value Gradient Descent in Machine Learning Likelihood Next-Token Prediction Similarity Scores Stochastic and Mini-batch Optimization Cross-Entropy Loss Weighted Aggregation The Language-Modeling Objective Contrastive Learning Self-Attention Pretraining The Attention Block Vision Transformers Contrastive Image-Text Pretraining Vision-Language Models