Ensemble Predicate Decoding for Unbiased Scene Graph Generation

• URL: http://arxiv.org/abs/2408.14187v1
• Date: Mon, 26 Aug 2024 11:24:13 GMT
• Title: Ensemble Predicate Decoding for Unbiased Scene Graph Generation
• Authors: Jiasong Feng, Lichun Wang, Hongbo Xu, Kai Xu, Baocai Yin,
• Abstract summary: Scene Graph Generation (SGG) aims to generate a comprehensive graphical representation that captures semantic information of a given scenario. The model's performance in predicting more fine-grained predicates is hindered by a significant predicate bias. This paper proposes Ensemble Predicate Decoding (EPD), which employs multiple decoders to attain unbiased scene graph generation.
• Score: 40.01591739856469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scene Graph Generation (SGG) aims to generate a comprehensive graphical representation that accurately captures the semantic information of a given scenario. However, the SGG model's performance in predicting more fine-grained predicates is hindered by a significant predicate bias. According to existing works, the long-tail distribution of predicates in training data results in the biased scene graph. However, the semantic overlap between predicate categories makes predicate prediction difficult, and there is a significant difference in the sample size of semantically similar predicates, making the predicate prediction more difficult. Therefore, higher requirements are placed on the discriminative ability of the model. In order to address this problem, this paper proposes Ensemble Predicate Decoding (EPD), which employs multiple decoders to attain unbiased scene graph generation. Two auxiliary decoders trained on lower-frequency predicates are used to improve the discriminative ability of the model. Extensive experiments are conducted on the VG, and the experiment results show that EPD enhances the model's representation capability for predicates. In addition, we find that our approach ensures a relatively superior predictive capability for more frequent predicates compared to previous unbiased SGG methods.

Related papers

• Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation [21.772806350802203]
  In scene graph generation (SGG) datasets, each subject-object pair is annotated with a single predicate. Existing SGG models are trained to predict the one and only predicate for each pair. This in turn results in the SGG models to overlook the semantic diversity that may exist in a predicate.
  arXiv  Detail & Related papers  (2024-07-22T05:53:46Z)
• Panoptic Scene Graph Generation with Semantics-Prototype Learning [23.759498629378772]
  Panoptic Scene Graph Generation (PSG) parses objects and predicts their relationships (predicate) to connect human language and visual scenes. Different language preferences of annotators and semantic overlaps between predicates lead to biased predicate annotations. We propose a novel framework named ADTrans to adaptively transfer biased predicate annotations to informative and unified ones.
  arXiv  Detail & Related papers  (2023-07-28T14:04:06Z)
• Unbiased Scene Graph Generation using Predicate Similarities [7.9112365100345965]
  Scene Graphs are widely applied in computer vision as a graphical representation of relationships between objects shown in images. These applications have not yet reached a practical stage of development owing to biased training caused by long-tailed predicate distributions. We propose a new classification scheme that branches the process to several fine-grained classifiers for similar predicate groups. The results of extensive experiments on the Visual Genome dataset show that the combination of our method and an existing debiasing approach greatly improves performance on tail predicates in challenging SGCls/SGDet tasks.
  arXiv  Detail & Related papers  (2022-10-03T13:28:01Z)
• CAME: Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation [10.724516317292926]
  We present a simple yet effective method called Context-Aware Mixture-of-Experts (CAME) to improve the model diversity and alleviate the biased scene graph generator. We have conducted extensive experiments on three tasks on the Visual Genome dataset to show that came achieved superior performance over previous methods.
  arXiv  Detail & Related papers  (2022-08-15T10:39:55Z)
• Adaptive Fine-Grained Predicates Learning for Scene Graph Generation [122.4588401267544]
  General Scene Graph Generation (SGG) models tend to predict head predicates and re-balancing strategies prefer tail categories. We propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. Our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-11T03:37:57Z)
• Pathologies of Pre-trained Language Models in Few-shot Fine-tuning [50.3686606679048]
  We show that pre-trained language models with few examples show strong prediction bias across labels. Although few-shot fine-tuning can mitigate the prediction bias, our analysis shows models gain performance improvement by capturing non-task-related features. These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior.
  arXiv  Detail & Related papers  (2022-04-17T15:55:18Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• Fine-Grained Predicates Learning for Scene Graph Generation [155.48614435437355]
  Fine-Grained Predicates Learning aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task. We introduce a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs. We then propose a Category Discriminating Loss and an Entity Discriminating Loss, which both contribute to distinguishing fine-grained predicates.
  arXiv  Detail & Related papers  (2022-04-06T06:20:09Z)
• PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation [58.98802062945709]
  We propose a novel Predicate-Correlation Perception Learning scheme to adaptively seek out appropriate loss weights. Our PCPL framework is further equipped with a graph encoder module to better extract context features.
  arXiv  Detail & Related papers  (2020-09-02T08:30:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.