SOTAVerified

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

Showing 51–75 of 82 papers

TitleStatusHype
Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation—0
On Exploring Undetermined Relationships for Visual Relationship Detection—0
Improving Visual Relation Detection using Depth MapsCode0
Visual Relationship Detection with Language prior and SoftmaxCode0
Target-Tailored Source-Transformation for Scene Graph Generation—0
Scene Graph Generation with External Knowledge and Image Reconstruction—0
Optimising the Input Image to Improve Visual Relationship Detection—0
On Class Imbalance and Background Filtering in Visual Relationship Detection—0
Visual Semantic Information Pursuit: A Survey—0
Graphical Contrastive Losses for Scene Graph ParsingCode1
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship DetectionCode0
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship DetectionCode0
An Interpretable Model for Scene Graph Generation—0
The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scaleCode0
Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge—0
A Problem Reduction Approach for Visual Relationships Detection—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions—0
Improving Information Extraction from Images with Learned Semantic Models—0
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph GenerationCode0
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning—0
Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation—0
Visual relationship detection with deep structural rankingCode0
Natural Language Guided Visual Relationship Detection—0
Attend and Interact: Higher-Order Object Interactions for Video Understanding—0
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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