Cross-Domain Object Recognition Using Few-Shot Learning
Abstract
Object recognition across diverse domains poses a challenge due to varying object appearances and environmental conditions. This research explores few-shot learning for cross-domain object recognition, focusing on leveraging small annotated datasets to train models on unseen domains. By combining meta-learning and transfer learning techniques, the proposed method enables accurate recognition in new domains with minimal labeled data. Extensive experiments on multiple cross-domain datasets demonstrate the effectiveness of the proposed approach in handling domain shift issues, making it ideal for real-world applications with limited labeled data.
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