CLAWS: Contrastive Learning with hard Attention and Weak Supervision

• URL: http://arxiv.org/abs/2112.00847v1
• Date: Wed, 1 Dec 2021 21:45:58 GMT
• Title: CLAWS: Contrastive Learning with hard Attention and Weak Supervision
• Authors: Jansel Herrera-Gerena, Ramakrishnan Sundareswaran, John Just, Matthew Darr, Ali Jannesari
• Abstract summary: We present CLAWS, an annotation-efficient learning framework, addressing the problem of manually labeling large-scale agricultural datasets. CLAWS uses a network backbone inspired by SimCLR and weak supervision to investigate the effect of contrastive learning within class clusters. We compare results between a supervised SimCLR and CLAWS using an agricultural dataset with 227,060 samples consisting of 11 different crop classes.
• Score: 1.1619569706231647
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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