COCO Image Labelling Tool - Frontend (FE) (ICCV'23 Paper)

COCO is a computer vision dataset with crowdsourced annotations. For every object of interest in each image, there is an instance-wise segmentation along with its class label, as well as image-wide description (caption). As detailed in the COCO report, the tool has been carefully designed to make the crowdsourced annotation process efficient. The instance segmentation annotation procedure consists of several steps, including the curation of candidate images, class labelling, instance labelling, and instance segmentation. In this repository, we focus on the class labelling stage. The annotator is presented one image at a time and tags the present classes in the image. This "tagging" paradigm for class labelling is arguably one of the most popular approaches to put semantic labels on images.

We open source the frontend (FE) modules for COCO class label annotation. Our FE is a reproduction of the original interface. This replicated annotation system has been used for the Neglected Free Lunch project, published as an ICCV'23 Paper.

Warning: The full annotation system works only when the backend is set up, which we do not support. However, the repository contains sufficient information for configuring the BE on your own.

Example view of the FE interface

Watch the videos below for an idea of how the interface works. For each page of the human intelligence task (HIT), the annotator is asked to drag and drop icons for categories that are present in the image. The worker then clicks the "Submit" button which will load the next page.

(Youtube video demo - click to open)

What are being recorded?

For each image, we record the following data structure. The data collected are much richer than the COCO annotations themselves. For example, our FE collects the time series of annotators' interactions with the images on the FE page. It also contains information about the icon location on the image and various timestamps and durations for interacting with the annotation tool.

{
  "image_id": 459214,
  "originalImageHeight": 428,
  "originalImageWidth": 640,
  "imageHeight": 450,
  "imageWidth": 450,
  "timeSpent": 22283,
  "actionHistories": [
    {"actionType": "add",
     "iconType": "pizza",
     "pointTo": {"x": 0.5839524517087668, "y": 0.5888888888888889},
     "timeAt": 16686},
    {"actionType": "add",
     "iconType": "cup",
     "pointTo": {"x": 0.5839524517087668, "y": 0.5888888888888889},
     "timeAt": 16686}
  ],
  "categoryHistories": [
    {"categoryIndex": 1,
     "categoryName": "Animal",
     "timeAt": 10815,
     "usingKeyboard": false},
    {"categoryIndex": 10,
     "categoryName": "IndoorObjects",
     "timeAt": 19415,
     "usingKeyboard": false}
  ],
  "mouseTracking": [
    {"timeAt": 15725,
     "x": 0.6790490341753344,
     "y": 0.8622222222222222},
    {"timeAt": 17426,
     "x": 0.7176820208023774,
     "y": 0.6422222222222222}
  ],
  "worker_id": "00AA3B5E80"
}

• image_id: COCO image identifier
• imageHeight, imageWidth: Number of pixels in the FE page
• timeSpent: Number of milliseconds spent on this page
• actionHistories: Time series of actions related to positioning the class icons
• categoryHistories: Time series of actions related to the superclass bar at the lower part of the page
• mouseTracking: Trajectory of mouse cursor over the image region
• worker_id: We STRONGLY SUGGEST to anonymise the workers AMT identifiers when utilising them in any form.

Overall architecture

Overall architecture for our COCO Annotation tool

1. Amazon Mechanical Turk (AMT) provides the Human Intelligence Task (HIT) identifiers for the current HIT via url (?hitDatasetName=ABCDEF&cocoHitId=abcdef012345)
2. Through API Gateway, the HIT identifiers are queried (hitDatasetName and cocoHitId).
3. The responsible DynamoDB (DDB) table returns the necessary information for building the frontend view (image url).
4. (and 5.) The AMT worker drags and drops the class icons on the corresponding object in the image.
5. (and 7.) The annotations are sent to the DDB tables (CocoAnnotation and CocoAnnotationPages).

Detailed update schedules for annotations across pages

• Each HIT consists of N pages of image selection tasks.
• Opening the Amplify page triggers the recording of basic information about the entire HIT on the CocoAnnotation table.
• Upon clicking on the Submit button on each page, the annotation data for the page are sent to the CocoAnnotationPages table.
• The CocoAnnotation and CocoAnnotationPages are associated through the Annotation ID column.

Building the frontend

Run

yarn install
yarn start

We have hosted the web page with AWS Amplify that has supported a CI/CD with the current repository.

More on the backend

We do not support BE in this repository. If you wish to actually build the whole architecture, you will need to configure the BE resources by yourself.

For your information, below is the list of BE resources we have used for the overall system.

Category	AWS Type	Resource Name	Description
Function	Lambda	CocoAPI	Functions for reading and writing on the DynamoDB Tables.
Api	API Gateway	CocoAPI	Routing for the CocoAPI functions.
Storage	DynamoDB	CocoHIT	DB for MTurk tasks (grouping of images into HITs).
Storage	DynamoDB	CocoAnnotation	DB for annotations per HIT (=N pages of annotation tasks). It contains reference to N entries in the CocoAnnotationPage table.
Storage	DynamoDB	CocoAnnotationPage	DB for annotations per page (single image).

Sufficient information for configuring your own BE is given at:

• The interface for the API access from the FE to DynamoDB is available at src/api/CocoAPI/*.ts.
• The required list of columns and corresponding types for DynamoDB tables are available at src/models/*.ts.

Amazon Mechanical Turk (AMT or MTurk)

The above web page can be integrated into the "Survey" tasks supported by AMT. When workers choose to work on a "Survey" task, they enter a landing page designed by the HIT requesters. We use the HTML file amt-question-form.html as the landing page. The page contains instructions as well as the url link to the Amplify page described above. The url is built automatically, given the requester-specified parameters: toolLink, version, hitDatasetName, and cocoHitId. They are defined by the requester in batch through a CSV database.

When annotations are completed, we use the AMT API to read and match the workers' task submission status on the AMT server and the annotation data on our DDB tables. We assess the sanity of submitted work and make accept/reject decisions for the submissions through the AMT API.

Acknowledgement

• Dante @1000ship is an amazing engineer who did most of the work in this repository.
• Seong Joon was asking for more and more features in the meantime..
• This is the result of great discussions with the great HCI and AI researchers:
  • Dongyoon
  • Jean
  • Junsuk
  • John
  • Minsuk
  • Sangdoo
• and funding from
  • @Naver-AI
  • DGIST Intelligence Augmentation Group (DIAG).
• We also thank the COCO authors, especially Tsung-Yi Lin, for their great paper and personal communications.

License

MIT license

Copyright (c) 2022-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
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The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
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THE SOFTWARE.

Citing our work

@inproceedings{han2023iccv,
  title = {Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts},
  author = {Han, Dongyoon and Choe, Junsuk and Chun, Seonghyeok and Chung, John Joon Young and Chang, Minsuk and Yun, Sangdoo and Song, Jean Y. and Oh, Seong Joon},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2023}
}