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

Dongyoon Han^1*, Junsuk Choe^2*, Seonghyeok Chun³, John Joon Young Chung⁴

Minsuk Chang⁵, Sangdoo Yun¹, Jean Y. Song⁶, Seong Joon Oh^7†

_{* 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.

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:

• ImageNet-AB (HuggingFace)
• COCO-AB (HuggingFace)

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).

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.

• Dataloader for ImageNet-AB: imagenet_dataloader.ipynb
• Dataloader for COCO-AB: coco_dataloader.ipynb

Annotation tools for ImageNet and COCO

• Annotation tool for ImageNet: github.com/naver-ai/imagenet-annotation-tool
• Annotation tool for COCO: github.com/naver-ai/coco-annotation-tool

License

MIT License

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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}
}