Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

Model Overview

Abstract

Scene graph generation is an important visual under-standing task with a broad range of vision applications.Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbi-ased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6,demonstrating its effectiveness and generality.

Publication
In Conference on Computer Vision and Pattern Recognition 2021
Rongjie Li
Rongjie Li
PhD Students

My research interests include scene understanding, deep learning, graph neural networks.

Songyang Zhang
Songyang Zhang
PhD Student

My research interests include few/low-shot learning, graph neural networks and video understanding.

Bo Wan
Bo Wan
Master Students

My research interests include visual scene understanding and vision-language.

Xuming He
Xuming He
Associate Professor

My research interests include few/low-shot learning, graph neural networks and video understanding.

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