Papers Scene Graph Classification
“Scene Graph Classification” 태그가 달린 논문 11편 · 필터 해제
Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge
This work introduces an enhanced approach to generating scene graphs by incorporating both a relationship hierarchy and commonsense knowledge. Specifically, we begin by proposing a hierarchical relation head that exploit…
Large Language ModelMultimodal Deep LearningPredicate ClassificationRelation+4HL-Net: Heterophily Learning Network for Scene Graph Generation
Scene graph generation (SGG) aims to detect objects and predict their pairwise relationships within an image. Current SGG methods typically utilize graph neural networks (GNNs) to acquire context information between obje…
Graph ClassificationGraph GenerationScene Graph ClassificationScene Graph GenerationFine-Grained Scene Graph Generation with Data Transfer
Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understand…
Graph GenerationPredicate ClassificationScene Graph ClassificationScene Graph Detection+2Relation Regularized Scene Graph Generation
Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects ar…
Graph ClassificationGraph GenerationObjectPredicate Classification+4Recovering the Unbiased Scene Graphs from the Biased Ones
Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects. Recently, more efforts have been paid to the long tail prob…
Missing LabelsScene Graph ClassificationScene Graph DetectionScene Graph Generation+3Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions
In this work, we seek new insights into the underlying challenges of the Scene Graph Generation (SGG) task. Quantitative and qualitative analysis of the Visual Genome dataset implies -- 1) Ambiguity: even if inter-object…
Bidirectional Relationship ClassificationDiagnosticGraph GenerationObject+3Unified Graph Structured Models for Video Understanding
Accurate video understanding involves reasoning about the relationships between actors, objects and their environment, often over long temporal intervals. In this paper, we propose a message passing graph neural network …
Action DetectionGraph ClassificationGraph Neural NetworkRelational Reasoning+2Energy-Based Learning for Scene Graph Generation
Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in a…
Graph GenerationInductive BiasScene Graph ClassificationScene Graph Detection+2Improving Scene Graph Classification by Exploiting Knowledge from Texts
Training scene graph classification models requires a large amount of annotated image data. Meanwhile, scene graphs represent relational knowledge that can be modeled with symbolic data from texts or knowledge graphs. Wh…
ClassificationGeneral ClassificationGraph ClassificationKnowledge Graphs+7Classification by Attention: Scene Graph Classification with Prior Knowledge
A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addressed this by relational reasoning over al…
ClassificationGeneral ClassificationGraph ClassificationInductive Bias+4Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
Machine understanding of complex images is a key goal of artificial intelligence. One challenge underlying this task is that visual scenes contain multiple inter-related objects, and that global context plays an importan…
Scene Graph ClassificationScene Graph GenerationStructured Prediction