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Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts | ICCV'23 Paper

Dongyoon Han1*, Junsuk Choe2*, Seonghyeok Chun3, John Joon Young Chung4

Minsuk Chang5, Sangdoo Yun1, Jean Y. Song6, Seong Joon Oh7†

* Equal contribution Corresponding author

1 NAVER AI LAB 2 Sogang University 3 Dante Company 4 University of Michigan 5 NAVER AI LAB, currently at Google 6 DGIST 7 University of Tübingen

Supervised learning of image classifiers distills human knowledge into a parametric model f through pairs of images and corresponding labels (X,Y). We argue that this simple and widely used representation of human knowledge neglects rich auxiliary information from the annotation procedure, such as the time-series of mouse traces and clicks.

imagenet-byproduct-sample

Our insight is that such annotation byproducts Z provide approximate human attention that weakly guides the model to focus on the foreground cues, reducing spurious correlations and discouraging shortcut learning.

We have created ImageNet-AB and COCO-AB to verify this:

They are ImageNet and COCO training sets enriched with sample-wise annotation byproducts, collected by replicating the respective original annotation tasks.

We refer to the new paradigm of training models with annotation byproducts as learning using annotation byproducts (LUAB).

luab

We show that a simple multitask loss for regressing Z together with Y already improves the generalisability and robustness of the learned models. Compared to the original supervised learning, LUAB does not require extra annotation costs.

Dataloader for ImageNet-AB and COCO-AB

We provide example dataloaders for the annotation byproducts.

Annotation tools for ImageNet and COCO

License

MIT License

Copyright (c) 2023-present NAVER Cloud Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 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}
}

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