Cluster-Aware Similarity Diffusion for Instance Retrieval
- URL: http://arxiv.org/abs/2406.02343v2
- Date: Thu, 6 Jun 2024 04:15:44 GMT
- Title: Cluster-Aware Similarity Diffusion for Instance Retrieval
- Authors: Jifei Luo, Hantao Yao, Changsheng Xu,
- Abstract summary: Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph.
We propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval.
- Score: 64.40171728912702
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph. However, existing techniques that construct the affinity graph based on pairwise instances can lead to the propagation of misinformation from outliers and other manifolds, resulting in inaccurate results. To overcome this issue, we propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval. The primary concept of CAS is to conduct similarity diffusion within local clusters, which can reduce the influence from other manifolds explicitly. To obtain a symmetrical and smooth similarity matrix, our Bidirectional Similarity Diffusion strategy introduces an inverse constraint term to the optimization objective of local cluster diffusion. Additionally, we have optimized a Neighbor-guided Similarity Smoothing approach to ensure similarity consistency among the local neighbors of each instance. Evaluations in instance retrieval and object re-identification validate the effectiveness of the proposed CAS, our code is publicly available.
Related papers
- Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data.
Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
arXiv Detail & Related papers (2024-04-24T09:04:36Z) - GCC: Generative Calibration Clustering [55.44944397168619]
We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure.
First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples.
Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
arXiv Detail & Related papers (2024-04-14T01:51:11Z) - Retrieval-Augmented Classification with Decoupled Representation [31.662843145399044]
We propose a $k$-nearest-neighbor (KNN)-based method for retrieval augmented classifications.
We find that shared representation for classification and retrieval hurts performance and leads to training instability.
We evaluate our method on a wide range of classification datasets.
arXiv Detail & Related papers (2023-03-23T06:33:06Z) - Unsupervised Hashing with Similarity Distribution Calibration [127.34239817201549]
Unsupervised hashing methods aim to preserve the similarity between data points in a feature space by mapping them to binary hash codes.
These methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space.
The similarity range is bounded by the code length and can lead to a problem known as similarity collapse.
This paper introduces a novel Similarity Distribution (SDC) method to alleviate this problem.
arXiv Detail & Related papers (2023-02-15T14:06:39Z) - Shift of Pairwise Similarities for Data Clustering [7.462336024223667]
We consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities.
This leads to shifting (adaptively) the pairwise similarities which might make some of them negative.
We then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem.
arXiv Detail & Related papers (2021-10-25T16:55:07Z) - Instance Similarity Learning for Unsupervised Feature Representation [83.31011038813459]
We propose an instance similarity learning (ISL) method for unsupervised feature representation.
We employ the Generative Adversarial Networks (GAN) to mine the underlying feature manifold.
Experiments on image classification demonstrate the superiority of our method compared with the state-of-the-art methods.
arXiv Detail & Related papers (2021-08-05T16:42:06Z) - Similarity Based Stratified Splitting: an approach to train better
classifiers [0.0]
We propose a Similarity-Based Stratified Splitting technique, which uses both the output and input space information to split the data.
We evaluate our proposal in twenty-two benchmark datasets with classifiers such as Multi-Layer Perceptron, Support Vector Machine, Random Forest and K-Nearest Neighbors.
arXiv Detail & Related papers (2020-10-13T01:07:48Z) - Near-Optimal Comparison Based Clustering [7.930242839366938]
We show that our method can recover a planted clustering using a near-optimal number of comparisons.
We empirically validate our theoretical findings and demonstrate the good behaviour of our method on real data.
arXiv Detail & Related papers (2020-10-08T12:03:13Z) - LSD-C: Linearly Separable Deep Clusters [145.89790963544314]
We present LSD-C, a novel method to identify clusters in an unlabeled dataset.
Our method draws inspiration from recent semi-supervised learning practice and proposes to combine our clustering algorithm with self-supervised pretraining and strong data augmentation.
We show that our approach significantly outperforms competitors on popular public image benchmarks including CIFAR 10/100, STL 10 and MNIST, as well as the document classification dataset Reuters 10K.
arXiv Detail & Related papers (2020-06-17T17:58:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.