SG-Tailor: Inter-Object Commonsense Relationship Reasoning for Scene Graph Manipulation

• URL: http://arxiv.org/abs/2503.18988v1
• Date: Sun, 23 Mar 2025 09:11:04 GMT
• Title: SG-Tailor: Inter-Object Commonsense Relationship Reasoning for Scene Graph Manipulation
• Authors: Haoliang Shang, Hanyu Wu, Guangyao Zhai, Boyang Sun, Fangjinhua Wang, Federico Tombari, Marc Pollefeys,
• Abstract summary: We introduce SG-Tailor, an autoregressive model that predicts the conflict-free relationship between any two nodes.<n>For edge modification, SG-Tailor employs a Cut-And-Stitch strategy to solve the conflicts and globally adjust the graph.
• Score: 73.76691480257851
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scene graphs capture complex relationships among objects, serving as strong priors for content generation and manipulation. Yet, reasonably manipulating scene graphs -- whether by adding nodes or modifying edges -- remains a challenging and untouched task. Tasks such as adding a node to the graph or reasoning about a node's relationships with all others are computationally intractable, as even a single edge modification can trigger conflicts due to the intricate interdependencies within the graph. To address these challenges, we introduce SG-Tailor, an autoregressive model that predicts the conflict-free relationship between any two nodes. SG-Tailor not only infers inter-object relationships, including generating commonsense edges for newly added nodes but also resolves conflicts arising from edge modifications to produce coherent, manipulated graphs for downstream tasks. For node addition, the model queries the target node and other nodes from the graph to predict the appropriate relationships. For edge modification, SG-Tailor employs a Cut-And-Stitch strategy to solve the conflicts and globally adjust the graph. Extensive experiments demonstrate that SG-Tailor outperforms competing methods by a large margin and can be seamlessly integrated as a plug-in module for scene generation and robotic manipulation tasks.

Related papers

• Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking [61.69892497726235]
  Composite Node Message Passing Network (CoNo-Link) is a framework for modeling ultra-long frames information for association. In addition to the previous method of treating objects as nodes, the network innovatively treats object trajectories as nodes for information interaction. Our model can learn better predictions on longer-time scales by adding composite nodes.
  arXiv  Detail & Related papers  (2023-12-14T14:00:30Z)
• Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network [3.9280441311534653]
  Scene graph generation (SGG) captures the relationships between objects in an image and creates a structured graph-based representation. Existing SGG methods have a limited ability to accurately predict detailed relationships. A new approach to the modeling multiobject relationships, called edge dual scene graph generation (EdgeSGG), is proposed herein.
  arXiv  Detail & Related papers  (2023-11-02T12:36:52Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• Graph Transformer GANs for Graph-Constrained House Generation [223.739067413952]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The GTGAN learns effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
  arXiv  Detail & Related papers  (2023-03-14T20:35:45Z)
• Refined Edge Usage of Graph Neural Networks for Edge Prediction [51.06557652109059]
  We propose a novel edge prediction paradigm named Edge-aware Message PassIng neuRal nEtworks (EMPIRE) We first introduce an edge splitting technique to specify use of each edge where each edge is solely used as either the topology or the supervision. In order to emphasize the differences between pairs connected by supervision edges and pairs unconnected, we further weight the messages to highlight the relative ones that can reflect the differences.
  arXiv  Detail & Related papers  (2022-12-25T23:19:56Z)
• Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
  Modelling long-range dependencies is critical for scene understanding tasks in computer vision. A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive. We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
  arXiv  Detail & Related papers  (2022-09-20T14:41:37Z)
• SoftEdge: Regularizing Graph Classification with Random Soft Edges [18.165965620873745]
  Graph data augmentation plays a vital role in regularizing Graph Neural Networks (GNNs) Simple edge and node manipulations can create graphs with an identical structure or indistinguishable structures to message passing GNNs but of conflict labels. We propose SoftEdge, which assigns random weights to a portion of the edges of a given graph to construct dynamic neighborhoods over the graph.
  arXiv  Detail & Related papers  (2022-04-21T20:12:36Z)
• FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding [39.527059564775094]
  Graph convolutional network (GCN) has been widely explored and used in non-Euclidean application domains. In this paper, we analyze the shrinking properties in the node embedding space at first, and then design a simple yet versatile method. Experiments on four real-world dynamic graph datasets compared with competitive baseline models demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2021-02-27T09:13:47Z)

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.