Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation

• URL: http://arxiv.org/abs/2308.05274v2
• Date: Mon, 25 Sep 2023 02:39:29 GMT
• Title: Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation
• Authors: Xinyu Lyu, Jingwei Liu, Yuyu Guo, Lianli Gao
• Abstract summary: We propose a novel DynSGG model based on multi-task learning, DynSGG-MTL, which introduces the local interaction information and global human-action interaction information. Long-temporal human actions supervise the model to generate multiple scene graphs that conform to the global constraints and avoid the model being unable to learn the tail predicates.
• Score: 51.92419880088668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of dynamic scene graph generation (DynSGG) aims to generate scene graphs for given videos, which involves modeling the spatial-temporal information in the video. However, due to the long-tailed distribution of samples in the dataset, previous DynSGG models fail to predict the tail predicates. We argue that this phenomenon is due to previous methods that only pay attention to the local spatial-temporal information and neglect the consistency of multiple frames. To solve this problem, we propose a novel DynSGG model based on multi-task learning, DynSGG-MTL, which introduces the local interaction information and global human-action interaction information. The interaction between objects and frame features makes the model more fully understand the visual context of the single image. Long-temporal human actions supervise the model to generate multiple scene graphs that conform to the global constraints and avoid the model being unable to learn the tail predicates. Extensive experiments on Action Genome dataset demonstrate the efficacy of our proposed framework, which not only improves the dynamic scene graph generation but also alleviates the long-tail problem.

Related papers

• Retrieval Augmented Generation for Dynamic Graph Modeling [15.09162213134372]
  Dynamic graph modeling is crucial for analyzing evolving patterns in various applications. Existing approaches often integrate graph neural networks with temporal modules or redefine dynamic graph modeling as a generative sequence task. We introduce the Retrieval-Augmented Generation for Dynamic Graph Modeling (RAG4DyG) framework, which leverages guidance from contextually and temporally analogous examples.
  arXiv  Detail & Related papers  (2024-08-26T09:23:35Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• Joint Generative Modeling of Scene Graphs and Images via Diffusion Models [37.788957749123725]
  We present a novel generative task: joint scene graph - image generation. We introduce a novel diffusion model, DiffuseSG, that jointly models the adjacency matrix along with heterogeneous node and edge attributes. With a graph transformer being the denoiser, DiffuseSG successively denoises the scene graph representation in a continuous space and discretizes the final representation to generate the clean scene graph.
  arXiv  Detail & Related papers  (2024-01-02T10:10:29Z)
• Unbiased Scene Graph Generation in Videos [36.889659781604564]
  We introduce TEMPURA: TEmporal consistency and Memory-guided UnceRtainty Attenuation for unbiased dynamic SGG. TEMPURA employs object-level temporal consistencies via transformer sequence modeling, learns to synthesize unbiased relationship representations. Our method achieves significant (up to 10% in some cases) performance gain over existing methods.
  arXiv  Detail & Related papers  (2023-04-03T06:10:06Z)
• Scene Graph Modification as Incremental Structure Expanding [61.84291817776118]
  We focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. We frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE) We construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets.
  arXiv  Detail & Related papers  (2022-09-15T16:26:14Z)
• Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs [15.614710220461353]
  We show that capturing long-term dependencies is the key to effective generation of dynamic scene graphs. Experimental results demonstrate that our Dynamic Scene Graph Detection Transformer (DSG-DETR) outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-18T03:02:11Z)
• Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling [2.3022070933226217]
  We propose a novel framework, namely static- and dynamic-graph learning-neural network (GL) The model acquires static and dynamic graph matrices from data to model long-term and short-term patterns respectively. It achieves state-of-the-art performance on almost all datasets.
  arXiv  Detail & Related papers  (2021-12-06T08:19:15Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• Unconditional Scene Graph Generation [72.53624470737712]
  We develop a deep auto-regressive model called SceneGraphGen which can learn the probability distribution over labelled and directed graphs. We show that the scene graphs generated by SceneGraphGen are diverse and follow the semantic patterns of real-world scenes.
  arXiv  Detail & Related papers  (2021-08-12T17:57:16Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)

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.