用不确定性建模提升图像检索的可靠性与精度
Evidential Transformers for Improved Image Retrieval
- 引入证据推理机制，以不确定度驱动图像检索
- 在SOP和CUB-200-2011上超越现有最佳结果
- 适合需要高可靠性的图像匹配场景
我们提出证据化变压器（Evidential Transformer），一种基于不确定性的变压器模型，用于改进内容驱动的图像检索（CBIR）。本文通过将概率方法融入图像检索，实现了更稳健、可靠的性能。证据分类显著优于传统多分类训练基准，在深度度量学习中表现更优。此外，借助全局上下文视觉变换器（GC ViT）架构，我们在多个数据集上提升了当前最优检索性能。实验结果一致表明该方法在斯坦福在线产品（SOP）和CUB-200-2011数据集的所有测试设置中均表现出可靠性，树立了新的基准。
原文摘要 · Abstract (English)
We introduce the Evidential Transformer, an uncertainty-driven transformer model for improved and robust image retrieval. In this paper, we make several contributions to content-based image retrieval (CBIR). We incorporate probabilistic methods into image retrieval, achieving robust and reliable results, with evidential classification surpassing traditional training based on multiclass classification as a baseline for deep metric learning. Furthermore, we improve the state-of-the-art retrieval results on several datasets by leveraging the Global Context Vision Transformer (GC ViT) architecture. Our experimental results consistently demonstrate the reliability of our approach, setting a new benchmark in CBIR in all test settings on the Stanford Online Products (SOP) and CUB-200-2011 datasets.
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