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Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection

Yajing Xu, Haitao Yang, Si Li, Xinyi Wang, Mingfei Cheng
2020 Applied Sciences  
due to long tail distribution of relationship.  ...  Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance.  ...  Visual relationship detection aims to recognize various visually observable predicates between subject and object, where subject and object are a pair of objects in the image.  ... 
doi:10.3390/app10031191 fatcat:sqmftpugwvemjkt5njy2gw6rsa

Towards Overcoming False Positives in Visual Relationship Detection [article]

Daisheng Jin, Xiao Ma, Chongzhi Zhang, Yizhuo Zhou, Jiashu Tao, Mingyuan Zhang, Haiyu Zhao, Shuai Yi, Zhoujun Li, Xianglong Liu, Hongsheng Li
2020 arXiv   pre-print
In this paper, we investigate the cause of the high false positive rate in Visual Relationship Detection (VRD).  ...  We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e.g., the inaccurate object detection, which  ...  Introduction Visual Relationship Detection (VRD) is an important visual task that bridges the gap between middle-level visual perception, e.g., object detection, and high-level visual understanding, e.g  ... 
arXiv:2012.12510v2 fatcat:6w65rb26uzfgxi3wbumb6uaq5e

Relationship Proposal Networks

Ji Zhang, Mohamed Elhoseiny, Scott Cohen, Walter Chang, Ahmed Elgammal
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
To detect all relationships, it would be inefficient to first detect all individual objects and then classify all pairs; not only is the number of all pairs quadratic, but classification requires limited  ...  A scene with many objects may have only a few individual interacting objects (e.g., in a party image with many people, only a handful of people might be speaking with each other).  ...  Introduction While object detection is progressing at an ever-faster rate, relatively little work has explored understanding visual relationships at a large scale with related objects visually grounded  ... 
doi:10.1109/cvpr.2017.555 dblp:conf/cvpr/ZhangECCE17 fatcat:cmbby5xp5rfmrmhggwlqqggu2q

Visual Relationship Detection With Deep Structural Ranking

Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Visual relationship detection aims to describe the interactions between pairs of objects.  ...  In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.  ...  Zero-shot Visual Relationship Detection Due to the long tail distribution of relationships, it is hard to collect the images for all the possible relationships.  ... 
doi:10.1609/aaai.v32i1.12274 fatcat:4rwvmimlmjclrdqbefq3tsik5a

Visual Relationship Detection with Contextual Information

Yugang Li Yongbin Wang, Yuting Zhu
2020 Computers Materials & Continua  
In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.  ...  Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding.  ...  A common approach that detects visual relationship is to use the statistical patterns of co-occurrence between objects and their spatial layout for inferring.  ... 
doi:10.32604/cmc.2020.07451 fatcat:pwmcakuwnne4jolwsrri6g37wa

Exploring Visual Relationship for Image Captioning [article]

Ting Yao, Yingwei Pan, Yehao Li, Tao Mei
2018 arXiv   pre-print
Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections.  ...  Specifically, we present Graph Convolutional Networks plus Long Short-Term Memory (dubbed as GCN-LSTM) architecture that novelly integrates both semantic and spatial object relationships into image encoder  ...  Image Captioning with Visual Relationship With the constructed graphs over the detected objects based on their spatial and semantic connections, we next discuss how to integrate the learnt visual relationships  ... 
arXiv:1809.07041v1 fatcat:v4g353t6wbhavb25or3ubp2bfy

Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation [article]

Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis
2017 arXiv   pre-print
Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the state-of-the-art methods  ...  Understanding visual relationships involves identifying the subject, the object, and a predicate relating them.  ...  Acknowledgement The research was supported by the Office of Naval Research under Grant N000141612713: Visual Common Sense Reasoning for Multi-agent Activity Prediction and Recognition.  ... 
arXiv:1707.09423v2 fatcat:yp6ke2lucnfpbm7bkyludcp6ki

Learning Effective Visual Relationship Detector on 1 GPU [article]

Yichao Lu, Cheng Chang, Himanshu Rai, Guangwei Yu, Maksims Volkovs
2019 arXiv   pre-print
Challenge task consists of detecting objects and identifying relationships between them in complex scenes.  ...  We present our winning solution to the Open Images 2019 Visual Relationship challenge. This is the largest challenge of its kind to date with nearly 9 million training images.  ...  Our pipeline consists of object detection followed by spatiosemantic and visual feature extraction, and a final aggregation phase where all information is combined to generate relationship prediction.  ... 
arXiv:1912.06185v1 fatcat:53sqbffve5dq3cbt5w2g7zzaxu

Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge [article]

Ji Zhang, Kevin Shih, Andrew Tao, Bryan Catanzaro, Ahmed Elgammal
2018 arXiv   pre-print
This article describes the model we built that achieved 1st place in the OpenImage Visual Relationship Detection Challenge on Kaggle.  ...  This baseline achieved the 2nd place when submitted; 2) spatial features are as important as visual features, especially for spatial relationships such as "under" and "inside of"; 3) It is a very effective  ...  We can see from Figure2 that we are able to correctly refer relationships, i.e., when there are multiple people playing multiple guitars, our model accurately points to the truly related pairs.  ... 
arXiv:1811.00662v2 fatcat:plvufcrqova7plywgi4m7uacuu

Exploring Visual Relationship for Image Captioning [chapter]

Ting Yao, Yingwei Pan, Yehao Li, Tao Mei
2018 Lecture Notes in Computer Science  
Technically, we build graphs over the detected objects in an image based on their spatial and semantic connections.  ...  Specifically, we present Graph Convolutional Networks plus Long Short-Term Memory (dubbed as GCN-LSTM) architecture that novelly integrates both semantic and spatial object relationships into image encoder  ...  Image Captioning with Visual Relationship With the constructed graphs over the detected objects based on their spatial and semantic connections, we next discuss how to integrate the learnt visual relationships  ... 
doi:10.1007/978-3-030-01264-9_42 fatcat:wydktnqxhjfobcgtobgmissaiq

DecAug: Augmenting HOI Detection via Decomposition [article]

Yichen Xie, Hao-Shu Fang, Dian Shao, Yong-Lu Li, Cewu Lu
2020 arXiv   pre-print
Further, we shift spatial correlation between humans and objects to other feasible configurations with the aid of a pose-guided Gaussian Mixture Model while preserving their interactions.  ...  Specifically, interactions with fewer samples enjoy more notable improvement. Our method can be easily integrated into various HOI detection models with negligible extra computational consumption.  ...  As a subtask of visual relationship detection, HOI detection pays attention to human-centric interactions with objects.  ... 
arXiv:2010.01007v1 fatcat:mcpola4mgjfhphmhl46mzp2gtm

Visual Relationship Detection with Language prior and Softmax [article]

Jaewon Jung, Jongyoul Park
2019 arXiv   pre-print
Visual relationship detection is an intermediate image understanding task that detects two objects and classifies a predicate that explains the relationship between two objects in an image.  ...  All experiments were only evaluated on Visual Relationship Detection and Visual Genome dataset.  ...  The proposed spatial vector is better than the spatial vector in [17] on visual relationship detection.  ... 
arXiv:1904.07798v1 fatcat:canqaigxvrdc5nj2dbtfsr3pf4

Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation

Gayoung Jung, Jonghun Lee, Incheol Kim
2021 Sensors  
The model uses a sliding window scheme to detect object tracklets of various lengths throughout the entire video.  ...  To improve the detection performance for sparse relationships, the proposed model applies a class weighting technique that adjusts the weight of sparse relationships to a higher level.  ...  When the visual context reasoning is completed, each object node in the visual context graph uses an object classifier and each relationship node uses a relationship classifier to calculate the class distribution  ... 
doi:10.3390/s21093164 pmid:34063299 pmcid:PMC8124611 fatcat:vtl2wizi5jfjdbqgxngtelwhwy

MR-NET: Exploiting Mutual Relation for Visual Relationship Detection

Yi Bin, Yang Yang, Chaofan Tao, Zi Huang, Jingjing Li, Heng Tao Shen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Inferring the interactions between objects, a.k.a visual relationship detection, is a crucial point for vision understanding, which captures more definite concepts than object detection.  ...  In this work, we propose a mutual relation net, dubbed MR-Net, to explore the mutual relation between paired objects for visual relationship detection.  ...  , and Li 2017) takes advantages of a variety of spatial distributions to infer visual relationships.  ... 
doi:10.1609/aaai.v33i01.33018110 fatcat:korw4rkgrfdsji3vhxg665g2au

Visual Relationship Forecasting in Videos [article]

Li Mi, Yangjun Ou, Zhenzhong Chen
2021 arXiv   pre-print
Specifically, given a subject-object pair with H existing frames, VRF aims to predict their future interactions for the next T frames without visual evidence.  ...  To meet this challenge, we present a new task named Visual Relationship Forecasting (VRF) in videos to explore the prediction of visual relationships in a reasoning manner.  ...  Different from visual relationship detection in static image [1, 2, 3, 4, 5, 6, 7] that focuses on detection object relationships based on a moment of observation, video visual relationship detection  ... 
arXiv:2107.01181v1 fatcat:ep2hjklh5zdxpaahmmakn5m3fy
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