---
title: "What's new in image understanding"
description: "Use Vision tap-to-segment, Foundation Models image inputs and image tool calling, and Vision on watchOS for richer app image understanding."
shoutout: "Unofficial, made with love by Superwall - the best way to monetize your apps."
category: "AI & Machine Learning"
---

# What's new in image understanding


[Watch on Apple Developer](https://developer.apple.com/videos/play/wwdc2026/237/)

Use Vision tap-to-segment, Foundation Models image inputs and image tool calling, and Vision on watchOS for richer app image understanding.

## TL;DR

- Vision adds tap-to-segment with iterative masks from taps, boxes, lassos, and scribbles, plus refinement by including or excluding points.
- Foundation Models can now accept image attachments directly in prompts for descriptive and generative image tasks such as captions, scene analysis, recipes, or suggestions.
- Foundation Models tool calling now supports image arguments through ImageReference, enabling custom image tools and built-in Vision tools such as barcode reading and OCR.
- Vision is now available on watchOS, with saliency analysis shown as a practical way to crop photos around prominent subjects for small screens.

## Overview

The session covers new image-understanding capabilities across Vision and the Foundation Models framework. Vision gains an interactive tap-to-segment API for isolating arbitrary objects, while Foundation Models gains image input support for large language model prompts.

The session also shows how image-based tool calling lets a language model delegate specific image tasks to external code or Vision tools, and how Vision can now be used in watchOS apps.

- Use Vision when you need fast, task-specific computer vision APIs such as segmentation, saliency, barcode scanning, OCR, pose estimation, detection, classification, tracking, or facial analysis.
- Use Foundation Models when the task benefits from flexible language reasoning over an image, such as captioning, describing a scene, or generating suggestions from visual context.
- Combine them with tool calling when a model needs deterministic or specialized image-processing capabilities.

## Segment images with tap-to-segment

Vision's new tap-to-segment API lets apps isolate any object in an image, not just people or predefined categories. Users can select objects with a point tap, bounding box, lasso, scribble, or combinations of those interactions.

Segmentation is iterative. After generating an initial mask, apps can refine the result by adding included points or excluding parts of the mask. The resulting mask is exposed as a pixel buffer indicating which pixels belong to the selected object.

- Create an ImageRequestHandler for the source image.
- Use GenerateIterativeSegmentationRequest with an initial seed such as a normalized point inside the object.
- Perform the request to receive a segmentation observation and pixel-buffer mask.
- Refine by adding included or excluded points and performing the same request again.
- Vision coordinates are normalized from 0 to 1 with the origin in the lower-left corner.
- For lasso input, use a stroke width at least 1% of the image width; very thin strokes may produce poor results.
- Before the first segmentation request on a device, the model asset must be downloaded; use downloadAssets and assetStatus to manage readiness.

### Segment and refine an object mask

Creates an iterative segmentation request from a seed point, performs it on an image, and refines the mask by adding another included point.

```swift
// Generate a segmentation mask of an object with a seed point
let handler = ImageRequestHandler(image)
let request = GenerateIterativeSegmentationRequest(seed: point)
let observation = try await handler.perform(request)
let mask = observation?.pixelBuffer

// Refine the mask with a new point
request.addIncludedPoint(newPoint)
let refinedObservation = try await handler.perform(request)
```

## Image inputs in Foundation Models

Foundation Models now supports image attachments in prompts. This enables large language model workflows that reason over images, such as generating captions, interpreting notes in a photo, describing a room, or creating a recipe from a fridge image.

The API uses the Foundation Models prompt builder syntax: provide text instructions and attach the image. The model then responds with generated content derived from both the text prompt and the image.

- Foundation Models is well suited to open-ended descriptive or reasoning tasks over images.
- Vision remains preferable for fixed, optimized computer-vision tasks, especially when performance matters or when processing video frames in real time.
- The two approaches can be combined by giving a Foundation Models session access to tools backed by Vision.

### Generate an image caption

Builds a prompt containing text instructions and an image attachment, then asks the language model session to generate a caption.

```swift
import FoundationModels

let prompt = Prompt {
    "Generate a caption for this image"
    Attachment(image)
}

let response = try await session.respond(to: prompt)
let caption = response.content
```

## Image-based tool calling

Tool calling lets a language model invoke external code when it needs information it cannot reliably produce by itself. This year, tool arguments can include image references, so a model can ask a tool to analyze an image already present in the session.

Image tool arguments use ImageReference rather than passing the full image directly. The reference is valid only in the context of the transcript where it was generated, so the tool resolves it through session history before converting the attachment to a pixel buffer or other analyzable representation.

- Define a Tool with @Generable arguments containing an ImageReference.
- Use @SessionProperty(\.history) to access the current session history.
- Create a Transcript from history and resolve the ImageReference in that transcript.
- Convert the resolved image attachment to a pixel buffer before passing it to custom image-analysis code.
- When prompting for image-based tool calls, label attached images so the model can identify which image to pass to the tool.

### Create a custom image-based tool

Shows the key pattern for image tool calling: receive an ImageReference, resolve it against session history, convert the attachment to image data, and run app-specific analysis.

```swift
import FoundationModels

struct PlantIdentifierTool: Tool {
    @SessionProperty(\.history) var history

    @Generable
    struct Arguments {
        var image: ImageReference
    }

    func call(arguments: Arguments) async throws -> String {
        let imageReference = arguments.image
        let transcript = Transcript(history)

        guard let imageAttachment = imageReference.resolve(in: transcript) else {
            throw AppError.imageNotFound
        }

        let image = try imageAttachment.pixelBuffer()
        return classifyPlant(image)
    }
}
```

### Use built-in Vision tools with a language model session

Configures a Foundation Models session with a Vision barcode reader tool and labels the attached flyer image so the model can pass it to the tool.

```swift
import FoundationModels
import Vision

let session = LanguageModelSession(model: model, tools: [BarcodeReaderTool()])

let response = try await session.respond(generating: EventInfo.self) {
    "Get the date, location, and website from this flyer"
    Attachment(image)
        .label("flyer")
}
```

## Built-in Vision tools and custom Vision-backed tools

Vision provides built-in tools for common model gaps. The barcode reader tool helps models read barcodes and QR codes, and the OCR tool helps read fine or dense text in more than 30 languages.

Apps can also build their own tools on top of Vision's broader image-analysis APIs. The session specifically mentions segmentation, facial analysis, pose estimation, detection, image classification, trajectory analysis, and object tracking as examples of Vision capabilities that can be exposed to a model through custom tools.

- Import Vision and FoundationModels when using Vision tools with a LanguageModelSession.
- Pass the desired tools when creating the session.
- Prefer a Vision tool when a model needs a precise capability such as barcode scanning or OCR rather than free-form visual reasoning.

## Vision on watchOS

Vision is now available on watchOS. The session demonstrates using saliency analysis in a watch app that displays wildlife photos, where the full image may be hard to understand on a small display.

GenerateObjectnessBasedSaliencyImageRequest identifies salient objects in an image. The app can use the most prominent object's bounding box as a crop rectangle so the watch UI emphasizes the subject rather than showing an unhelpful full-frame image.

- Create a saliency request with GenerateObjectnessBasedSaliencyImageRequest.
- Perform the request on a CGImage.
- Read salientObjects from the returned observation.
- Use the first or most prominent salient object as a NormalizedRect crop.

### Generate a crop around a prominent subject

Uses Vision saliency analysis to find a prominent object and return its normalized bounding rectangle for cropping, including on watchOS.

```swift
func generateImageCrop(in image: CGImage) async throws -> NormalizedRect? {
    let request = GenerateObjectnessBasedSaliencyImageRequest()
    let observation = try await request.perform(on: image)
    let prominentObjects = observation.salientObjects
    return prominentObjects.first
}
```

## Resources

- [Segmenting objects using taps, scribbles or rectangles](https://developer.apple.com/documentation/Vision/segmenting-objects-using-taps-scribbles-or-rectangles)
- [Implementing saliency-based image cropping in iOS and watchOS](https://developer.apple.com/documentation/Vision/implementing-saliency-based-image-cropping-in-iOS-and-watchOS)

## Related Sessions

- [What's new in the Foundation Models framework](https://wwdc.ai/2026/241)
- [Deep dive into the Foundation Models framework](https://developer.apple.com/videos/play/wwdc2025/301)
- [Discover Swift enhancements in the Vision framework](https://developer.apple.com/videos/play/wwdc2024/10163)

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