Situational Scene Graph for Structured Human-centric Situation Understanding

• URL: http://arxiv.org/abs/2410.22829v1
• Date: Wed, 30 Oct 2024 09:11:25 GMT
• Title: Situational Scene Graph for Structured Human-centric Situation Understanding
• Authors: Chinthani Sugandhika, Chen Li, Deepu Rajan, Basura Fernando,
• Abstract summary: We propose a graph-based representation called Situational Scene Graph (SSG) to encode both humanobject-relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. We will release the code and the dataset soon.
• Score: 15.91717913059569
• License:
• Abstract: Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capturing the human-object relationships while ignoring fine-grained semantic properties of the action components. These semantic properties are crucial for understanding the current situation, such as where does the action takes place, what tools are used and functional properties of the objects. In this work, we propose a graph-based representation called Situational Scene Graph (SSG) to encode both human-object relationships and the corresponding semantic properties. The semantic details are represented as predefined roles and values inspired by situation frame, which is originally designed to represent a single action. Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task. Given that the existing datasets are not applicable to the task, we further introduce a SSG dataset whose annotations consist of semantic role-value frames for human, objects and verb predicates of human-object relations. Finally, we demonstrate the effectiveness of our proposed SSG representation by testing on different downstream tasks. Experimental results show that the unified representation can not only benefit predicate classification and semantic role-value classification, but also benefit reasoning tasks on human-centric situation understanding. We will release the code and the dataset soon.

Related papers

• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• Learning Action-Effect Dynamics from Pairs of Scene-graphs [50.72283841720014]
  We propose a novel method that leverages scene-graph representation of images to reason about the effects of actions described in natural language. Our proposed approach is effective in terms of performance, data efficiency, and generalization capability compared to existing models.
  arXiv  Detail & Related papers  (2022-12-07T03:36:37Z)
• Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing [17.63475613154152]
  This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints in a scene graph. A scene graph is an efficient and structured way to represent all the objects and their semantic relationships in the image.
  arXiv  Detail & Related papers  (2022-11-03T16:46:46Z)
• Image Semantic Relation Generation [0.76146285961466]
  Scene graphs can distil complex image information and correct the bias of visual models using semantic-level relations. In this work, we introduce image semantic relation generation (ISRG), a simple but effective image-to-text model.
  arXiv  Detail & Related papers  (2022-10-19T16:15:19Z)
• RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments [72.04891523115535]
  We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
  arXiv  Detail & Related papers  (2022-07-16T12:40:17Z)
• Relation Regularized Scene Graph Generation [206.76762860019065]
  Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations. We propose a relation regularized network (R2-Net) which can predict whether there is a relationship between two objects. Our R2-Net can effectively refine object labels and generate scene graphs.
  arXiv  Detail & Related papers  (2022-02-22T11:36:49Z)
• Adaptive Attentional Network for Few-Shot Knowledge Graph Completion [16.722373937828117]
  Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.
  arXiv  Detail & Related papers  (2020-10-19T16:27:48Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction [42.37557498737781]
  We propose a new and simple architecture named Rich and Fair semantic extraction network (RiFa) RiFa predicts subject-object relations based on both the visual and semantic features of entities under certain contextual area. Experiments on the popular Visual Genome dataset show that RiFa achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-02-01T09:28:44Z)

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.