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Visual Relationship Detection

Visual relationship detection (VRD) is one newly developed computer vision task aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition and is essential for fully understanding images, even the visual world.

Papers

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Showing 1–50 of 82 papers

TitleStatusHype
METOR: A Unified Framework for Mutual Enhancement of Objects and Relationships in Open-vocabulary Video Visual Relationship DetectionCode0
End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting—0
Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship DetectionCode1
Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection—0
Video Relationship Detection Using Mixture of ExpertsCode0
RelVAE: Generative Pretraining for few-shot Visual Relationship Detection—0
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box ReconstructionCode0
STUPD: A Synthetic Dataset for Spatial and Temporal Relation ReasoningCode0
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection—0
Unified Visual Relationship Detection with Vision and Language ModelsCode0
Image Semantic Relation Generation—0
Distance-Aware Occlusion Detection with Focused AttentionCode1
Neural Message Passing for Visual Relationship DetectionCode1
Learning Structured Representations of Visual Scenes—0
VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection—0
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Scene Graph Generation: A Comprehensive Survey—0
A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection—0
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship DetectionCode0
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation—0
Image Scene Graph Generation (SGG) BenchmarkCode0
Spatial-Temporal Transformer for Dynamic Scene Graph GenerationCode1
Recovering the Unbiased Scene Graphs from the Biased OnesCode1
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries—0
2.5D Visual Relationship DetectionCode1
RelTransformer: A Transformer-Based Long-Tail Visual Relationship RecognitionCode1
A Comprehensive Survey of Scene Graphs: Generation and Application—0
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship DetectionCode1
Towards Overcoming False Positives in Visual Relationship Detection—0
LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videosCode1
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection TasksCode1
Visualization of Contributions to Open-Source ProjectsCode0
Constructing a Visual Relationship Authenticity DatasetCode0
Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal RepresentationsCode1
Bounding-box Channels for Visual Relationship Detection—0
Explanation-based Weakly-supervised Learning of Visual Relations with Graph NetworksCode1
Hierarchical Graph Attention Network for Visual Relationship Detection—0
Fixed-size Objects Encoding for Visual Relationship Detection—0
Visual Relationship Detection using Scene Graphs: A Survey—0
CPARR: Category-based Proposal Analysis for Referring Relationships—0
Exploring Long Tail Visual Relationship Recognition with Large VocabularyCode1
ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks—0
AVR: Attention based Salient Visual Relationship DetectionCode0
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks—0
NODIS: Neural Ordinary Differential Scene UnderstandingCode1
Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition—0
Visual Relationship Detection with Relative Location MiningCode0
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships—0
Deep Contextual Attention for Human-Object Interaction Detection—0
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image InterpretationCode1
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All datasetsVRD Relationship DetectionVRD Phrase DetectionVRD Predicate DetectionVisual GenomeVRD

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Yu et. al [[Yu et al.2017a]]R@10031.89—Unverified
2vrd-dsrR@10023.29—Unverified
3BLOCKR@10020.96—Unverified
4Dai et. al [[Dai, Zhang, and Lin2017]]R@10020.88—Unverified
5Liang et. al [[Liang, Lee, and Xing2017]]R@10020.79—Unverified
6Peyre et. al [[Peyre et al.2017]]R@10017.1—Unverified
7Zhang et. al [[Hanwang Zhang2017]]R@10015.2—Unverified
8Lu et. al [[Lu et al.2016]]R@10014.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Yu et. al [[Yu et al.2017a]]R@10029.43—Unverified
2BLOCKR@10028.96—Unverified
3Dai et. al [[Dai, Zhang, and Lin2017]]R@10023.45—Unverified
4Liang et. al [[Liang, Lee, and Xing2017]]R@10022.6—Unverified
5Zhang et. al [[Hanwang Zhang2017]]R@10022.42—Unverified
6Peyre et. al [[Peyre et al.2017]]R@10019.5—Unverified
7Lu et. al [[Lu et al.2016]]R@10017.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Yu et. al [[Yu et al.2017a]]R@10094.65—Unverified
2vrd-dsrR@10093.18—Unverified
3BLOCKR@10092.58—Unverified
4Dai et. al [[Dai, Zhang, and Lin2017]]R@10081.9—Unverified
5Peyre et. al [[Peyre et al.2017]]R@10052.6—Unverified
6Lu et. al [[Lu et al.2016]]R@10047.87—Unverified
7Zhang et. al [[Hanwang Zhang2017]]R@10044.76—Unverified
#ModelMetricClaimedVerifiedStatus
1PEVLR@10066.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Ours - vR@50 k=115—Unverified