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Implementation of "Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions".
In this work, we seek new insights into the underlying challenges of the Scene Graph Generation (SGG) task. Quantitative and qualitative analysis of the Visual Genome dataset implies -- 1) Ambiguity: even if inter-object relationship contains the same object (or predicate), they may not be visually or semantically similar, 2) Asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies, and 3) Higher-order contexts: leveraging the identities of certain graph elements can help to generate accurate scene graphs.
Motivated by the analysis, we design a novel SGG framework, Local-to-Global Interaction Networks (LOGIN). Locally, interactions extract the essence between three instances - subject, object, and background - while baking direction awareness into the network by constraining the input order. Globally, interactions encode the contexts between every graph components -- nodes and edges. Also we introduce Attract & Repel loss which finely adjusts predicate embeddings. Our framework enables predicting the scene graph in a local-to-global manner by design, leveraging the possible complementariness. To quantify how much LOGIN is aware of relational direction, we propose a new diagnostic task called Bidirectional Relationship Classification (BRC). We see that LOGIN can successfully distinguish relational direction than existing methods (in BRC task) while showing state-of-the-art results on the Visual Genome benchmark (in SGG task).
@article{woo2022tackling,
title={Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions},
author={Woo, Sangmin and Noh, Junhyug and Kim, Kangil},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2022},
publisher={IEEE}
}
We appreciate much the nicely organized codes developed by Scene-Graph-Benchmark and Graph R-CNN. Our codebase is built mostly based on them.