Research map / Graph learning / Graph algorithms and clustering

FC-DGCN: Deep Graph Convolutional Network for Face Clustering and Recognition

NeurIPS 2026 · Graph learning › Graph algorithms and clustering · approach: Neural and local graph clustering

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes FC-DGCN, a deep graph convolutional network for face clustering on affinity graphs, aimed at balancing clustering quality and scalability on large-scale face data.

Problem

Face clustering on large unlabeled face datasets is needed for annotation and recognition, but existing GCN-based methods struggle to balance clustering quality with scalability to large-scale data.

Approach

A deep graph convolutional network applied to affinity graphs for face clustering, intended to improve quality while remaining scalable; specific mechanism not stated in the available abstract.

Closest papers by meaning

• Learning to Cluster Faces via Confidence and Connectivity Estimation (CVPR 2020 · similarity 0.93)
• Structure-Aware Face Clustering on a Large-Scale Graph With 107 Nodes (CVPR 2021 · similarity 0.91)
• Linkage Based Face Clustering via Graph Convolution Network (CVPR 2019 · similarity 0.91)
• Learning to Cluster Faces on an Affinity Graph (CVPR 2019 · similarity 0.91)
• Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space (ICLR 2022 · similarity 0.89)
• Density-Aware Feature Embedding for Face Clustering (CVPR 2020 · similarity 0.87)