Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning

• URL: http://arxiv.org/abs/2412.13875v1
• Date: Wed, 18 Dec 2024 14:16:40 GMT
• Title: Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning
• Authors: Jaeyoon Kim, Yoonki Cho, Taeyong Kim, Sung-Eui Yoon,
• Abstract summary: We propose a complementary denoising method based on Continuous Conditional Random Field (C-CRF) We demonstrate the complementarity of our method through its application to three visual re-ranking methods.
• Score: 15.880661910248827
• License:
• 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).

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