Pair then Relation: Pair-Net for Panoptic Scene Graph Generation
Jinghao Wang, Zhengyu Wen, Xiangtai Li, Zujin Guo, Jingkang Yang,, Ziwei Liu

TL;DR
This paper introduces Pair-Net, a novel framework for Panoptic Scene Graph generation that improves relationship prediction by focusing on pair-wise object relationships, achieving significant performance gains over previous methods.
Contribution
The paper proposes Pair-Net, a new approach that enhances PSG by explicitly modeling pair-wise relationships with a Pair Proposal Network and a lightweight Matrix Learner.
Findings
Over 10% absolute improvement over PSGFormer baseline
Effective modeling of pair-wise relationships enhances PSG performance
Code is publicly available for reproducibility
Abstract
Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications. The goal of this work aims to design a novel and strong baseline for PSG. To achieve that, we first conduct an in-depth analysis to identify the bottleneck of the current PSG models, finding that inter-object pair-wise recall is a crucial factor that was ignored by previous PSG methods. Based on this and the recent query-based frameworks, we present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
