A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Contextual Coefficients Excitation Feature: Focal Visual Representation for Relationship Detection
2020
Applied Sciences
Preserved Fulltext
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2020
Computers Materials & Continua
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2018
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 266,094 results