CLAWS: Contrastive Learning with hard Attention and Weak Supervision

TL;DR

CLAWS is a contrastive learning framework that uses hard attention and weak supervision to improve visual representations for agricultural datasets, enabling effective clustering and analysis with minimal labeling.

Contribution

It introduces a novel contrastive learning method with hard attention masks and weak supervision, enhancing clustering quality in large-scale agricultural image datasets.

Findings

Achieves a competitive NMI score of 0.7325 on agricultural data

Creates low-dimensional, well-defined clusters for large datasets

Requires minimal parameter tuning for effective representation learning

Abstract

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.