SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction

by   Dayang Wang, et al.

Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress is hindered by the lack of a rapid and reliable methodology to evaluate browning. Current methods to identify and quantify browning are either too subjective, labor intensive, or inaccurate. In this paper, we report a deep learning model for lettuce browning prediction. To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model includes quadratic features in the transformer model which is more powerful to incorporate real-world representations than the linear transformer. Second, a multi-scale training strategy is proposed to augment the data and explore more of the inherent self-similarity of the lettuce images. Third, the proposed model uses a siamese architecture which learns the inter-relations among the limited training samples. Fourth, the model is pretrained on the ImageNet and then trained with the reptile meta-learning algorithm to learn higher-order gradients than a regular one. Experiment results on the fresh-cut lettuce datasets show that the proposed SQ-Swin outperforms the traditional methods and other deep learning-based backbones.


page 1

page 3

page 4

page 5

page 6

page 7


A Few-Shot Meta-Learning based Siamese Neural Network using Entropy Features for Ransomware Classification

Ransomware defense solutions that can quickly detect and classify differ...

Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]

Automated per-instance algorithm selection often outperforms single lear...

A Transformer-Based Siamese Network for Change Detection

This paper presents a transformer-based Siamese network architecture (ab...

N-shot Palm Vein Verification Using Siamese Networks

The use of deep learning methods to extract vascular biometric patterns ...

Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification

Colonoscopic Polyp Re-Identification aims to match the same polyp from a...

A Ransomware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework

Solutions for rapid prioritization of different ransomware have been rai...

Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

The Transformer-Kernel (TK) model has demonstrated strong reranking perf...

Please sign up or login with your details

Forgot password? Click here to reset