  Learning effective visual representations without human supervision is a
long-standing problem in computer vision. Recent advances in self-supervised
learning algorithms have utilized contrastive learning, with methods such as
SimCLR, which applies a composition of augmentations to an image, and minimizes
a contrastive loss between the two augmented images. In this paper, we present
CLAWS, an annotation-efficient learning framework, addressing the problem of
manually labeling large-scale agricultural datasets along with potential
applications such as anomaly detection and plant growth analytics. CLAWS uses a
network backbone inspired by SimCLR and weak supervision to investigate the
effect of contrastive learning within class clusters. In addition, we inject a
hard attention mask to the cropped input image before maximizing agreement
between the image pairs using a contrastive loss function. This mask forces the
network to focus on pertinent object features and ignore background features.
We compare results between a supervised SimCLR and CLAWS using an agricultural
dataset with 227,060 samples consisting of 11 different crop classes. Our
experiments and extensive evaluations show that CLAWS achieves a competitive
NMI score of 0.7325. Furthermore, CLAWS engenders the creation of low
dimensional representations of very large datasets with minimal parameter
tuning and forming well-defined clusters, which lends themselves to using
efficient, transparent, and highly interpretable clustering methods such as
Gaussian Mixture Models.
