Adaptive Self-training Framework for Fine-grained Scene Graph Generation

• URL: http://arxiv.org/abs/2401.09786v1
• Date: Thu, 18 Jan 2024 08:10:34 GMT
• Title: Adaptive Self-training Framework for Fine-grained Scene Graph Generation
• Authors: Kibum Kim, Kanghoon Yoon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park
• Abstract summary: Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets. We introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets. Our experiments verify the effectiveness of ST-SGG on various SGG models.
• Score: 31.12694282340842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets such as the long-tailed predicate distribution and missing annotation problems. In this work, we aim to alleviate the long-tailed problem of SGG by utilizing unannotated triplets. To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained. While there has been significant progress in self-training for image recognition, designing a self-training framework for the SGG task is more challenging due to its inherent nature such as the semantic ambiguity and the long-tailed distribution of predicate classes. Hence, we propose a novel pseudo-labeling technique for SGG, called Class-specific Adaptive Thresholding with Momentum (CATM), which is a model-agnostic framework that can be applied to any existing SGG models. Furthermore, we devise a graph structure learner (GSL) that is beneficial when adopting our proposed self-training framework to the state-of-the-art message-passing neural network (MPNN)-based SGG models. Our extensive experiments verify the effectiveness of ST-SGG on various SGG models, particularly in enhancing the performance on fine-grained predicate classes.

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