Papers Scene Graph Generation
“Scene Graph Generation” 태그가 달린 논문 365편 · 필터 해제
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms
A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals. Semantic modeling within operating …
Graph GenerationScene Graph GenerationAUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation
Scene graph generation (SGG) aims to understand the visual objects and their semantic relationships from one given image. Until now, lots of SGG datasets with the eyelevel view are released but the SGG dataset with the o…
Graph GenerationRelationship DetectionScene Graph GenerationORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling
Every day, countless surgeries are performed worldwide, each within the distinct settings of operating rooms (ORs) that vary not only in their setups but also in the personnel, tools, and equipment used. This inherent di…
Data AugmentationGraph GenerationLanguage ModellingScene Graph GenerationSportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos
Video-based visual relation detection tasks, such as video scene graph generation, play important roles in fine-grained video understanding. However, current video visual relation detection datasets have two main limitat…
Graph GenerationRelationScene Graph GenerationVideo scene graph generation+2Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-labeling
Learning to build 3D scene graphs is essential for real-world perception in a structured and rich fashion. However, previous 3D scene graph generation methods utilize a fully supervised learning manner and require a larg…
3d scene graph generationGraph GenerationGraph Neural NetworkScene Graph GenerationEGTR: Extracting Graph from Transformer for Scene Graph Generation
Scene Graph Generation (SGG) is a challenging task of detecting objects and predicting relationships between objects. After DETR was developed, one-stage SGG models based on a one-stage object detector have been actively…
Graph GenerationMulti-Task LearningObjectobject-detection+4From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models
Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Despite recent advancements, existing methods struggle to generate scene graphs with nov…
Graph GenerationImage to textRelationScene Graph Generation+1Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection
Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional label assignment for training Transformer-ba…
RelationRelationship DetectionScene Graph GenerationVisual Relationship DetectionPredicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement
Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between objects. This complexity and diversity in SGG leads to underrepresenta…
DiversityGraph GenerationRelationScene Graph Generation+1R3CD: Scene Graph to Image Generation with Relation-aware Compositional Contrastive Control Diffusion
Image generation tasks have achieved remarkable performance using large-scale diffusion models. However, these models are limited to capturing the abstract relations (viz., interactions excluding positional relations) am…
DenoisingImage GenerationRelationScene Graph Generation+1DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semant…
Graph GenerationGraph MatchingPanoptic Scene Graph GenerationRelation+2HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual dat…
Scene Graph GenerationMapping High-level Semantic Regions in Indoor Environments without Object Recognition
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive s…
Graph GenerationLanguage ModelingLanguage ModellingObject Recognition+2Towards Scene Graph Anticipation
Spatio-temporal scene graphs represent interactions in a video by decomposing scenes into individual objects and their pair-wise temporal relationships. Long-term anticipation of the fine-grained pair-wise relationships …
Graph GenerationLong Term AnticipationScene Graph GenerationSpatio-temporal Scene GraphsS^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR
Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR). However, previous works have primarily relied on multi-stage learning, where the…
Graph Generationobject-detectionObject DetectionPose Estimation+1Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning
Scene graph generation (SGG) endeavors to predict visual relationships between pairs of objects within an image. Prevailing SGG methods traditionally assume a one-off learning process for SGG. This conventional paradigm …
Graph GenerationIn-Context LearningPrompt LearningScene Graph GenerationSGTR+: End-to-end Scene Graph Generation with Transformer
Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers fro…
graph constructionGraph GenerationScene Graph GenerationTD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation
Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several issues, including 1) Contextual noise, a…
DenoisingGraph GenerationObjectPredicate Classification+1Adaptive Self-training Framework for Fine-grained Scene Graph Generation
Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets such as the long-tailed predicate distribution and missing annotation problems. In this work, we aim to alleviate …
Graph GenerationScene Graph GenerationJoint Generative Modeling of Scene Graphs and Images via Diffusion Models
In this paper, we present a novel generative task: joint scene graph - image generation. While previous works have explored image generation conditioned on scene graphs or layouts, our task is distinctive and important a…
Graph GenerationImage GenerationScene Graph DetectionScene Graph Generation