Abstract
Visual re-ranking using Nearest Neighbor graph~(NN graph) has been adapted to yield high retrieval accuracy, since it is beneficial to exploring an high-dimensional manifold and applicable without additional fine-tuning. The quality of visual re-ranking using NN graph, however, is limited to that of connectivity, i.e., edges of the NN graph. Some edges can be misconnected with negative images. This is known as a noisy edge problem, resulting in a degradation of the retrieval quality. To address this, we propose a complementary denoising method based on Continuous Conditional Random Field (C-CRF) that uses a statistical distance of our similarity-based distribution. This method employs the concept of cliques to make the process computationally feasible. We demonstrate the complementarity of our method through its application to three visual re-ranking methods, observing quality boosts in landmark retrieval and person re-identification (re-ID).
Abstract (translated)
使用最近邻图（NN 图）进行视觉重新排序已被调整以获得较高的检索准确性，因为它有助于探索高维流形，并且无需额外的微调即可应用。然而，基于 NN 图的视觉重新排序的质量受限于其连通性，即 NN 图中的边。某些边可能会错误地连接到负面图像上，这被称为噪声边问题，导致检索质量下降。为了解决这一问题，我们提出了一种基于连续条件随机场（C-CRF）的互补去噪方法，该方法使用基于相似度分布的统计距离。此方法利用了团的概念以使过程在计算上可行。通过将我们的方法应用于三种视觉重新排序方法中，我们展示了其互补性，并观察到了地标检索和行人再识别（re-ID）质量上的提升。
URL
https://arxiv.org/abs/2412.13875
