Introduction to Vision Language Models (VLM)

Video Description

In this lecture from the Transformers for Vision series, we take a clear and practical first step into multi-modal AI, where models learn to understand images and text together instead of treating them as two separate worlds. If you have already seen how a Vision Transformer works for images and how large language models work for text, this session will help you connect the dots and understand why real data is multi-modal and why Vision Language Models are needed to make AI useful for tasks that mix visuals and words. What you will learn - The difference between single-modal models and multi-modal models and why Vision Transformers alone are not enough for captions, retrieval, and question answering - The core idea of a shared or joint embedding space where matched image and text pairs sit close together and mismatched pairs sit far apart - How popular models like CLIP, VisualBERT, and ViLBERT approach alignment using different fusion strategies - The roles of image encoders and text encoders and how they generate embeddings that can be aligned through training - Early fusion vs late fusion vs cross attention fusion and when each approach makes sense Key concepts explained - Image encoder choices such as CNNs and Vision Transformers and how both can produce meaningful visual embeddings - Text encoder choices such as BERT like encoders and GPT like decoders and how they map tokens to vectors - Contrastive learning as used in CLIP for aligning image and text embeddings by maximizing the similarity of true pairs and minimizing the similarity of false pairs - Joint embedding space and why semantic neighborhoods like animals or fruits emerge naturally after training Applications you will see - Image captioning where the input is an image and the output is a natural language sentence - Visual question answering where the inputs are an image and a question and the output is a grounded answer - Image retrieval where the input is a text query and the model retrieves the most relevant images from a gallery - Vision language action and vision language planning where multi-modal understanding feeds decisions in robotics and self driving systems Fusion strategies in simple terms - Early fusion - concatenate or merge text and image embeddings early and pass them through a transformer or MLP - Late fusion - keep encoders independent and compare only at the loss stage to enforce alignment - Cross attention fusion - let image tokens attend to text tokens and text tokens attend to image tokens across layers What comes next in the series - A focused lecture on contrastive learning and the CLIP training objective - A hands-on session where we build a nano vision language model from scratch using a small synthetic image caption dataset - A deeper paper walkthrough for CLIP and a brief tour of BLIP, Flamingo, and LLaVA style systems If you are learning with us, kindly like the video, subscribe to the channel, and turn on notifications. It helps us bring more long form lectures and hands on sessions for learners who want a strong foundation without shortcuts.