TMLR Journal 2022 Journal Article
Object-aware Cropping for Self-Supervised Learning
- Shlok Kumar Mishra
- Anshul Shah
- Ankan Bansal
- Janit K Anjaria
- Abhyuday Narayan Jagannatha
- Abhishek Sharma
- David Jacobs
- Dilip Krishnan
A core component of the recent success of self-supervised learning is cropping data augmentation, which selects sub-regions of an image to be used as positive views in the self-supervised loss. The underlying assumption is that randomly cropped and resized regions of a given image share information about the objects of interest, which is captured by the learned representation. This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image. However, in other datasets such as OpenImages or COCO, which are more representative of real world uncurated data, there are typically multiple small objects in an image. In this work, we show that self-supervised learning based on the usual random cropping performs poorly on such datasets (measured by the difference from fully-supervised learning). Instead of using pairs of random crops, we propose to leverage an unsupervised object proposal technique; the first view is a crop obtained from this algorithm, and the second view is a dilated version of the first view. This encourages the self-supervised model to learn both object and scene level semantic representations. Using this approach, which we call object-aware cropping, results in significant improvements over random scene cropping on classification and object detection benchmarks. For example, for pre-training on OpenImages, our approach achieves an improvement of 8.8% mAP over random scene cropping (both meth- ods using MoCo-v2). We also show significant improvements on COCO and PASCAL-VOC object detection and segmentation tasks over the state-of-the-art self-supervised learning approaches. Our approach is efficient, simple and general, and can be used in most existing contrastive and non-contrastive self-supervised learning frameworks.