The content discusses the significance of learning dual pose-invariant embeddings for object recognition and retrieval tasks. It introduces a novel approach with attention-based architecture and specially designed loss functions to optimize performance on challenging multi-view datasets.
The authors emphasize the importance of disentangling category-based learning from object-identity-based learning to achieve superior results in both recognition and retrieval tasks. They demonstrate significant improvements over previous methods, especially in single-view scenarios.
By training a network using pose-invariant losses that focus on clustering instances within the same category while separating them from other categories, the proposed method achieves remarkable accuracy gains across different datasets.
Ablation studies highlight the effectiveness of different loss components in improving performance for category-based and object-based tasks. The optimization of intra-class and inter-class distances further enhances the discriminative capabilities of the learned embeddings.
Overall, the study provides valuable insights into enhancing pose-invariant object recognition and retrieval through innovative architectural design and loss function optimization.
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by Rohan Sarkar... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00272.pdfDeeper Inquiries