Vision language models: what they see
Know how AI models read images, what they get right and wrong, and how to check their answers
A taste of a lesson
I uploaded a photo of my bookshelf and asked how many books there are. It said 47. I counted 63. Why so far off?
Counting is a known weak spot. The model receives the photo as a limited set of image tokens, often at reduced resolution, and thin book spines blur together. It also does not count one by one the way you would; it produces a plausible number in one go. You can improve it by cropping the shelf into sections and asking for each separately, or by asking it to list the titles it can read and counting those. Still verify by hand if the number matters. Quick exercise: which other bookshelf question would you expect it to answer well?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain in plain words how a vision language model turns an image into an answer
- Predict which image tasks these models handle well and which they often get wrong
- Write image prompts that reduce guessing and invented details
- Verify image based answers with quick, practical checks
- Protect personal and sensitive information when uploading images
Lesson plan
- 1 How the model reads an image Understand the encoder, adapter and language model pipeline in plain words. Start
- 2 What they do well Identify tasks where vision language models are reliably useful. Start
- 3 Where they fail, often confidently Recognise counting, spatial, small text and hallucination failures. Start
- 4 Prompting with images Ask questions in ways that reduce guessing and improve accuracy. Start
- 5 Checking the answer Build quick verification habits proportionate to the stakes. Start
- 6 Privacy and responsible use Decide what is safe to upload and how to reduce exposure. Start
Try asking
About this tutor
For anyone who uploads images to AI assistants or builds with multimodal models, from curious users to developers. You will learn in plain words how these models work: an image encoder turns the picture into tokens that a language model reads alongside your text, and image resolution and tiling decide how much detail survives. Then you look at real strengths, such as describing scenes, reading clear documents and summarising charts, and real weaknesses, such as counting, fine spatial relations, small text and confidently describing objects that are not there. The final lessons teach prompting techniques for images, simple verification habits and the privacy questions every uploaded photo raises.
Reviews
4.7
3 ratingsSample
- Matteo B.Sample
Good for a developer too. The tiling explanation finally made sense of why small text fails at low resolution.
- Adaeze O.Sample
Honest about the hallucinated objects problem. I tested it on my kitchen photo and it invented a kettle, just as the lesson predicted.
- Ruth G.Sample
I use image upload for work receipts and charts. The lesson on asking for exact quotes and marking unreadable parts cut down a lot of invented numbers.
About the teacher
Computer vision taught through real images, real failure cases and careful evaluation
9 tutors 406 lessons taught Sample
I teach computer vision: classification, detection, segmentation, document understanding, video and the newer models that combine images with language. Most of my work has been building vision systems that had to hold up outside the lab, under odd lighting, unusual cameras and labels that were not quite consistent. So my lessons spend as much time on data and evaluation as...
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