SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation

• URL: http://arxiv.org/abs/2412.06138v1
• Date: Mon, 09 Dec 2024 01:39:46 GMT
• Title: SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation
• Authors: Qiyu Liao, Xin Yuan, Min Xu, Dadong Wang,
• Abstract summary: We introduce Sequence Generative Image Augmentation (SGIA) for augmenting Fine-Grained Visual Classification (FGVC) datasets. Our method features a unique Bridging Transfer Learning process, designed to minimize the domain gap between real and synthetically augmented data. Our work sets a new benchmark and outperforms the previous state-of-the-art models in classification accuracy by 0.5% for the CUB-200-2011 dataset.
• Score: 16.642582574494742
• License:
• Abstract: In Fine-Grained Visual Classification (FGVC), distinguishing highly similar subcategories remains a formidable challenge, often necessitating datasets with extensive variability. The acquisition and annotation of such FGVC datasets are notably difficult and costly, demanding specialized knowledge to identify subtle distinctions among closely related categories. Our study introduces a novel approach employing the Sequence Latent Diffusion Model (SLDM) for augmenting FGVC datasets, called Sequence Generative Image Augmentation (SGIA). Our method features a unique Bridging Transfer Learning (BTL) process, designed to minimize the domain gap between real and synthetically augmented data. This approach notably surpasses existing methods in generating more realistic image samples, providing a diverse range of pose transformations that extend beyond the traditional rigid transformations and style changes in generative augmentation. We demonstrate the effectiveness of our augmented dataset with substantial improvements in FGVC tasks on various datasets, models, and training strategies, especially in few-shot learning scenarios. Our method outperforms conventional image augmentation techniques in benchmark tests on three FGVC datasets, showcasing superior realism, variability, and representational quality. Our work sets a new benchmark and outperforms the previous state-of-the-art models in classification accuracy by 0.5% for the CUB-200-2011 dataset and advances the application of generative models in FGVC data augmentation.

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