Papers Scene Graph Generation
“Scene Graph Generation” 태그가 달린 논문 365편 · 필터 해제
SANGRIA: Surgical Video Scene Graph Optimization for Surgical Workflow Prediction
Graph-based holistic scene representations facilitate surgical workflow understanding and have recently demonstrated significant success. However, this task is often hindered by the limited availability of densely annota…
DisentanglementGraph GenerationScene Graph GenerationFine-Grained Scene Graph Generation via Sample-Level Bias Prediction
Scene Graph Generation (SGG) aims to explore the relationships between objects in images and obtain scene summary graphs, thereby better serving downstream tasks. However, the long-tailed problem has adversely affected t…
Generative Adversarial NetworkGraph GenerationScene Graph GenerationBCTR: Bidirectional Conditioning Transformer for Scene Graph Generation
Scene Graph Generation (SGG) remains a challenging task due to its compositional property. Previous approaches improve prediction efficiency through end-to-end learning. However, these methods exhibit limited performance…
Graph GenerationScene Graph GenerationSemantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
The scene graph generation (SGG) task involves detecting objects within an image and predicting predicates that represent the relationships between the objects. However, in SGG benchmark datasets, each subject-object pai…
DiversityGraph GenerationScene Graph GenerationUnbiased Scene Graph GenerationOpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models
Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their relations, enabling the structured understanding of an image. Previous methods focus on predicting predefined object and relation categori…
Graph Generationobject-detectionObject DetectionPanoptic Scene Graph Generation+5From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) aims to generate a comprehensive graph-structure representation based on panoptic segmentation masks. Despite remarkable progress in PSG, almost all existing methods neglect the impo…
Graph GenerationKnowledge DistillationPanoptic Scene Graph GenerationPanoptic Segmentation+1A Fair Ranking and New Model for Panoptic Scene Graph Generation
In panoptic scene graph generation (PSGG), models retrieve interactions between objects in an image which are grounded by panoptic segmentation masks. Previous evaluations on panoptic scene graphs have been subject to an…
Graph GenerationPanoptic Scene Graph GenerationPanoptic SegmentationScene Graph GenerationESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding
Scene graphs have been proven to be useful for various scene understanding tasks due to their compact and explicit nature. However, existing approaches often neglect the importance of maintaining the symmetry-preserving …
Graph GenerationGraph Neural NetworkScene Graph GenerationScene UnderstandingEnhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation
This work focuses on training dataset enhancement of informative relational triplets for Scene Graph Generation (SGG). Due to the lack of effective supervision, the current SGG model predictions perform poorly for inform…
Graph GenerationScene Graph GenerationTripletSkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding
Remote Sensing Large Multi-Modal Models (RSLMMs) are developing rapidly and showcase significant capabilities in remote sensing imagery (RSI) comprehension. However, due to the limitations of existing datasets, RSLMMs ha…
Graph GenerationRelationScene Graph GenerationSTAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery
Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and the…
Graph GenerationObjectobject-detectionObject Detection+1ReCon1M:A Large-scale Benchmark Dataset for Relation Comprehension in Remote Sensing Imagery
Scene Graph Generation (SGG) is a high-level visual understanding and reasoning task aimed at extracting entities (such as objects) and their interrelationships from images. Significant progress has been made in the stud…
Graph Generationobject-detectionObject DetectionRelation+1Semantic Similarity Score for Measuring Visual Similarity at Semantic Level
Semantic communication, as a revolutionary communication architecture, is considered a promising novel communication paradigm. Unlike traditional symbol-based error-free communication systems, semantic-based visual commu…
Graph GenerationGraph MatchingMS-SSIMScene Graph Generation+4Leveraging Predicate and Triplet Learning for Scene Graph Generation
Scene Graph Generation (SGG) aims to identify entities and predict the relationship triplets \textit{\textless subject, predicate, object\textgreater } in visual scenes. Given the prevalence of large visual variations of…
Graph GenerationRelationScene Graph GenerationTransfer Learning+1CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
Video scene graph generation (VidSGG) has emerged as a transformative approach to capturing and interpreting the intricate relationships among objects and their temporal dynamics in video sequences. In this paper, we int…
Graph GenerationScene Graph GenerationScene UnderstandingVideo scene graph generationOED: Towards One-stage End-to-End Dynamic Scene Graph Generation
Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines, which typically consist of objec…
Graph Generationobject-detectionObject DetectionRelation Classification+1REACT: Real-time Efficiency and Accuracy Compromise for Tradeoffs in Scene Graph Generation
Scene Graph Generation (SGG) is a task that encodes visual relationships between objects in images as graph structures. SGG shows significant promise as a foundational component for downstream tasks, such as reasoning fo…
Graph GenerationObjectobject-detectionObject Detection+4Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency
Scene graph generation (SGG) is an important task in image understanding because it represents the relationships between objects in an image as a graph structure, making it possible to understand the semantic relationshi…
Graph GenerationScene Graph Generation4D Panoptic Scene Graph Generation
We are living in a three-dimensional space while moving forward through a fourth dimension: time. To allow artificial intelligence to develop a comprehensive understanding of such a 4D environment, we introduce 4D Panopt…
4D Panoptic SegmentationGraph GenerationLanguage ModellingLarge Language Model+4A Review and Efficient Implementation of Scene Graph Generation Metrics
Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metri…
BenchmarkingGraph GenerationScene Graph Generation