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

Kibum Kim, Kanghoon Yoon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park

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Keywords: Scene Graph Generation, Scene Understanding, Imbalanced Classification, Self-training, Long-tailed Problem

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TL;DR: This work proposes a novel self-training method for scene graph generation that assigns pseudo-labels for unannotated triplets to enhance the scene representation, which is challenging to adopt due to the unique nature of scene graph generation.

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 **S**elf-**T**raining 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 **C**lass-specific **A**daptive **T**hresholding with **M**omentum **(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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Primary Area: representation learning for computer vision, audio, language, and other modalities

Submission Number: 4310