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學年
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113 |
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學期
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2 |
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發表日期
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2025-06-25 |
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作品名稱
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Research on Performance Improvement of Vision Transformer Model Based on BEiT |
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作品名稱（其他語言）
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著者
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Zhe-Wei Liu, Chii-Jen Chen* |
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作品所屬單位
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出版者
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會議名稱
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The International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology (CICET 2025) |
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會議地點
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New Taipei, Taiwan |
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摘要
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Vision Transformer (ViT) has demonstrated exceptional performance in image classification tasks across large-scale datasets. However, its application in domain-specific or small-scale datasets remains a challenge. This research explores an alternative approach to image patch generation, replacing the fixed-size patch mechanism in ViT with semantic-aware segmentation using the Segment Anything Model (SAM). We focus on applying this technique to datasets such as marine biology, animals, and plants, where semantic consistency plays a more critical role. The segmented patches are compared to the conventional 16×16 patches used in ViT to evaluate their potential to enhance semantic representation. Preliminary results suggest that using SAM-based patches can introduce better localized and meaningful features, providing a foundation for performance enhancement in downstream tasks. |
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關鍵字
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Vision Transformer, BEiT, Semantic Segmentation, Small Datasets, Self-Supervised Learning |
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語言
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en_US |
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收錄於
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會議性質
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國際 |
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校內研討會地點
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淡水校園 |
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研討會時間
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20250625~20250627 |
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通訊作者
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國別
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TWN |
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公開徵稿
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出版型式
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出處
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