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Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Finally, an Inter-Domain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts ...
Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. ...
Learning the Graph from Features. This scheme is almost the same as the one used in the Intra-Domain Reasoning Module. The visual feature is transformed to the latent space z. ...
doi:10.1609/aaai.v34i07.6937
fatcat:vlulmyim2bgxjekytexsmviube
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
[article]
2020
arXiv
pre-print
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Finally, an InterDomain Transfer Module is proposed to exploit diverse transfer dependencies across all domains and enhance the regional feature representation by attending and transferring semantic contexts ...
Then an Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN. ...
Learning the Graph from Features. This scheme is almost the same as the one used in the Intra-Domain Reasoning Module. The visual feature is transformed to the latent space z. ...
arXiv:2002.07417v1
fatcat:jju32xvo5zbe3ma3bzpzz6r2ku
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
[article]
2021
arXiv
pre-print
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In particular, Graphonomy learns the global and structured semantic coherency in multiple domains via semantic-aware graph reasoning and transfer, enforcing the mutual benefits of the parsing across domains ...
The former extracts the semantic graph in each domain to improve the feature representation learning by propagating information with the graph; the latter exploits the dependencies among the graphs from ...
domains via graph transfer learning to achieve multiple levels of human parsing tasks. ...
arXiv:2101.10620v1
fatcat:hnbuqiugsfhvbc7phn5htmsvcy
Graphonomy: Universal Human Parsing via Graph Transfer Learning
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across ...
This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and ...
Additionally, we build an Inter-Graph Transfer module to attentively distill related semantics from the graph in one domain/task to the one in another domain, which bridges the semantic labels from different ...
doi:10.1109/cvpr.2019.00763
dblp:conf/cvpr/Gong0LS0L19
fatcat:rv5xgqag4jcghmzffi3s67fp2u
Graphonomy: Universal Human Parsing via Graph Transfer Learning
[article]
2019
arXiv
pre-print
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In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across ...
This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and ...
Additionally, we build an Inter-Graph Transfer module to attentively distill related semantics from the graph in one domain/task to the one in another domain, which bridges the semantic labels from different ...
arXiv:1904.04536v1
fatcat:di2yce3ytbhadml5lljt7yn66m
Graph Distillation for Action Detection with Privileged Modalities
[article]
2018
arXiv
pre-print
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Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. ...
On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. ...
Fig. 4 shows example distillation graphs learned on NTU RGB+D. The results show that our method, without transfer learning, is effective for action classification in the source domain. ...
arXiv:1712.00108v2
fatcat:vbvqueff4faahg365uzzgoffoq
Learning Robust Data Representation: A Knowledge Flow Perspective
[article]
2020
arXiv
pre-print
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It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain ...
Thus, robust representation learning by removing noisy features or samples, complementing incomplete data, and mitigating the distribution difference becomes the key. ...
., visual features and semantic features that are highly coupled. ...
arXiv:1909.13123v2
fatcat:wll23rkrznejvhzsihc6rwcwve
A Survey on Visual Transfer Learning using Knowledge Graphs
[article]
2022
arXiv
pre-print
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This survey focuses on visual transfer learning approaches using KGs. ...
We explain the notion of feature extractor, while specifically referring to visual and semantic features. ...
Zero-Shot Learning is a visual transfer learning task with labeled source domain data and unlabeled target domain data. ...
arXiv:2201.11794v1
fatcat:tapql5h4j5dvrnxjkaxek2cquu
A survey on visual transfer learning using knowledge graphs
2022
Semantic Web Journal
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This survey focuses on visual transfer learning approaches using KGs, as we believe that KGs are well suited to store and represent any kind of auxiliary knowledge. ...
We explain the notion of feature extractor, while specifically referring to visual and semantic features. ...
Acknowledgements This publication was created as part of the research project "KI Delta Learning" (project number: 19A19013D) funded by the Federal Ministry for Economic Affairs and Energy (BMWi) on the ...
doi:10.3233/sw-212959
fatcat:f4s43if3nbcxxfvrbtpdrrs2ry
Semi-supervised Breast Histological Image Classification by Node-attention Graph Transfer Network
2020
IEEE Access
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Importantly, the parameters θ r is shared across domains, which can be trained by the transfer learning with excellent feature learning capability both for source and target domains [11] , [19] . ...
graph convolutional network on the learned CNN features. ...
doi:10.1109/access.2020.3020149
fatcat:ppgxgdb7m5btlh4jz7y24ggrvi
Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
[article]
2020
arXiv
pre-print
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WAPPO outperforms the prior state-of-the-art in visual transfer and successfully transfers policies across Visual Cartpole and two instantiations of 16 OpenAI Procgen environments. ...
We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO), a novel algorithm for visual transfer in Reinforcement Learning that explicitly learns to align the distributions of extracted ...
Visual Cartpole and Procgen Easy are evaluated across 5 trials and Procgen Hard is evaluated across 3 trials. There is one source domain and one target domain per trial. ...
arXiv:2006.03465v1
fatcat:yfobpv4tdrhp5njilmgygf5qnu
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
[article]
2022
arXiv
pre-print
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Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. ...
Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic ...
transferred across domains. ...
arXiv:2205.08772v1
fatcat:kgzhmx5y3bbxjnaragigbsd3tq
Transferability of Brain decoding using Graph Convolutional Networks
[article]
2020
bioRxiv
pre-print
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The transferability of learned graph representations were evaluated under different circumstances, including knowledge transfer across cognitive domains, between different groups of subjects, and among ...
Our results indicate that in contrast to natural images, the scanning condition, instead of task domain, has a larger impact on feature transfer for medical imaging. ...
The transfer model achieved the highest decoding accuracy on the Motor task (92.5%) where the five types of body movements can be visually separated after the projection of learned graph representations ...
doi:10.1101/2020.06.21.163964
fatcat:334vgbbki5ccxajrrh7mdfa7a4
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
[article]
2022
arXiv
pre-print
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To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. ...
Existing methods for single network learning cannot solve this problem due to the domain shift across networks. ...
The Laplacian smoothing on node features with graph convolution in the representation learner enables an easy knowledge transfer across networks. ...
arXiv:1909.01541v3
fatcat:uspa64hdvnapbbxcntlorobdoi
Towards Cross-Domain Learning for Social Video Popularity Prediction
2013
IEEE transactions on multimedia
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Index Terms-Cross-domain media retrieval, social media, transfer learning, Twitter, video popularity. 1520-9210 © 2013 IEEE ...
We develop a transfer learning algorithm that can learn topics from social streams allowing us to model the social prominence of video content and improve popularity predictions in the video domain. ...
The transfer graph presents a unified graph structure to represent the task of transfer learning from social domain to video domain. ...
doi:10.1109/tmm.2013.2265079
fatcat:jycldmgh7rcv7a52ii5ay7oz4i
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