How Vision-Language Models Distinguish Between Multiple Objects

No — there is no detection step, and no list of discrete objects.

A vision-language model doesn't segment the image into objects first and then reason over that list, the way a detector + downstream classifier would. Instead:

1. The image is split into a grid of fixed-size patches (e.g. 16×16 pixels), and each patch is embedded into a "visual token" — the same shape as a text token.
2. Those visual tokens, plus a positional embedding per patch, are fed into the transformer alongside your text prompt.
3. Self-attention lets the text tokens in your question ("dog," "collar") attend more strongly to whichever patch tokens have a learned visual representation that's semantically close to "dog" — a similarity inherited from the vision encoder's contrastive pretraining on (image, caption) pairs.
4. The model generates its answer token-by-token, pulling information from the most relevant patches as it goes.

So "knowing which item is the dog" isn't detection — it's attention weighted by learned visual-semantic similarity and patch position. That's why it works well when objects are visually distinct (a dog looks very different from a bowl) and gets much weaker when objects look nearly identical (two same-colored mugs) — the patch representations for the two mugs are close together in embedding space, so there's little signal to tell them apart unless you add a disambiguating cue: position ("the mug on the left"), a distinguishing feature, or a crop that isolates just one of them.

Practical implication: if a product needs reliable "which exact item" answers among visually similar objects, don't rely on the VLM's raw attention. Run a cheap detector/segmenter first to localize candidates, then either crop each one into its own image or overlay numbered markers (set-of-mark prompting) before asking the VLM — turning an ambiguous discrimination problem into an unambiguous per-region question.