OOWL500: Overcoming Dataset Collection Bias in the Wild

by   Brandon Leung, et al.

The hypothesis that image datasets gathered online "in the wild" can produce biased object recognizers, e.g. preferring professional photography or certain viewing angles, is studied. A new "in the lab" data collection infrastructure is proposed consisting of a drone which captures images as it circles around objects. Crucially, the control provided by this setup and the natural camera shake inherent to flight mitigate many biases. It's inexpensive and easily replicable nature may also potentially lead to a scalable data collection effort by the vision community. The procedure's usefulness is demonstrated by creating a dataset of Objects Obtained With fLight (OOWL). Denoted as OOWL500, it contains 120,000 images of 500 objects and is the largest "in the lab" image dataset available when both number of classes and objects per class are considered. Furthermore, it has enabled several of new insights on object recognition. First, a novel adversarial attack strategy is proposed, where image perturbations are defined in terms of semantic properties such as camera shake and pose. Indeed, experiments have shown that ImageNet has considerable amounts of pose and professional photography bias. Second, it is used to show that the augmentation of in the wild datasets, such as ImageNet, with in the lab data, such as OOWL500, can significantly decrease these biases, leading to object recognizers of improved generalization. Third, the dataset is used to study questions on "best procedures" for dataset collection. It is revealed that data augmentation with synthetic images does not suffice to eliminate in the wild datasets biases, and that camera shake and pose diversity play a more important role in object recognition robustness than previously thought.


page 2

page 3


Monkeypox Image Data collection

This paper explains the initial Monkeypox Open image data collection pro...

Bugs in the Data: How ImageNet Misrepresents Biodiversity

ImageNet-1k is a dataset often used for benchmarking machine learning (M...

Contemplating real-world object classification

Deep object recognition models have been very successful over benchmark ...

Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition

Object pose increases interclass object variance which makes object reco...

Friction Variability in Auto-collected Dataset of Planar Pushing Experiments and Anisotropic Friction

Friction plays a key role in manipulating objects. Most of what we do wi...

Semantically Meaningful View Selection

An understanding of the nature of objects could help robots to solve bot...

Probing the Effect of Selection Bias on NN Generalization with a Thought Experiment

Learned networks in the domain of visual recognition and cognition impre...

Please sign up or login with your details

Forgot password? Click here to reset