Amazon Technologies, Inc.

Tianjun Xiao

Zheng Zhang

Wei Xia

Stefano Soatto

Yuanjun Xiong

Yifan Xing

Tong He

David Paul Wipf

Yongxin Wang

Techniques for performing visual clustering with a hierarchical graph neural network framework including a joint linkage prediction and density estimation graph model are described. Embodiments herein recurrently run the joint linkage prediction and density estimation graph model to generate intermediate clusters in multiple iterations (e.g., until convergence) to obtain a final clustering result. In certain embodiments, for each iteration, the input graph contains nodes that are merged from nodes assigned to intermediate clusters from the previous iteration. By using a small and fixed bandwidth k in each iteration, embodiments herein alleviate the sensitivity to the k selection for different clustering applications. Certain embodiments herein remove the tuning of a different k (e.g., k-bandwidth) for k-nearest neighbor graph construction over different clustering applications.

Hierarchical graph neural networks for visual clustering

Growing community of inventors

Nanjing, China

Tianjun Xiao