Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers
This addresses the problem of scarce labeled data for fine-grained classification, particularly in domains like e-commerce, though it appears incremental as it combines existing techniques.
The paper tackled fine-grained classification with limited annotated data by proposing Semi-ViT, a visual transformer fine-tuned using semi-supervised learning, which outperformed traditional CNNs and ViTs in such scenarios.
Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive representations of visual data using self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine tuned using semi-supervised learning techniques, suitable for situations where we have lack of annotated data. This is particularly common in e-commerce, where images are readily available but labels are noisy, nonexistent, or expensive to obtain. Our results demonstrate that Semi-ViT outperforms traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned with limited annotated data. These findings indicate that Semi-ViTs hold significant promise for applications that require precise and fine-grained classification of visual data.