2024 [ nach oben ]

1.
Otholt, J., Meinel, C., Yang, H.: Guided Cluster Aggregation: A Hierarchical Approach to Generalized Category Discovery. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024). pp. 2618–2627 (2024).
[ BibTeX ] [ Details ]
 
@inproceedings{Otholt_2024_WACV, author = {Otholt, Jona and Meinel, Christoph and Yang, Haojin}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)}, keywords = {myown}, month = {January}, pages = {2618-2627}, title = {Guided Cluster Aggregation: A Hierarchical Approach to Generalized Category Discovery}, year = 2024 }
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2023 [ nach oben ]

1.
Simsek, F., Pfitzmann, B., Raetz, H., Otholt, J., Yang, H., Meinel, C.: DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents. Proceedings of the 7th International Workshop on Historical Document Imaging and Processing. pp. 103–108. ACM (2023).
[ BibTeX ] [ Details ]
 
@inproceedings{DBLP:conf/icdar/SimsekPROYM23, author = {Simsek, Furkan and Pfitzmann, Brian and Raetz, Hendrik and Otholt, Jona and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing}, keywords = {myown}, pages = {103–108}, publisher = {ACM}, title = {DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents}, year = 2023 }
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URNhttp://dx.doi.org/10.1145/3604951.3605512

2022 [ nach oben ]

1.
Bartz, C., Raetz, H., Otholt, J., Meinel, C., Yang, H.: Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data. 2022 26th International Conference on Pattern Recognition (ICPR). pp. 3878–3884 (2022).
[ BibTeX ] [ Details ]
 
@inproceedings{9956471, author = {Bartz, Christian and Raetz, Hendrik and Otholt, Jona and Meinel, Christoph and Yang, Haojin}, booktitle = {2022 26th International Conference on Pattern Recognition (ICPR)}, keywords = {myown its}, pages = {3878-3884}, title = {Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data}, year = 2022 }
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URNhttp://dx.doi.org/10.1109/ICPR56361.2022.9956471

2.
Li, ziyun, Otholt, J., Dai, B., Hu, D., Meinel, C., Yang, H.: A Closer Look at Novel Class Discovery from the Labeled Set. Workshop on Distribution Shifts: Connecting Methods and Applications, NeurIPS 2022 (2022).
[ BibTeX ] [ Details ]
 
@inproceedings{li2022novel, author = {Li, ziyun and Otholt, Jona and Dai, Ben and Hu, Di and Meinel, Christoph and Yang, Haojin}, booktitle = {Workshop on Distribution Shifts: Connecting Methods and Applications, NeurIPS 2022}, keywords = {myown}, title = {A Closer Look at Novel Class Discovery from the Labeled Set}, year = 2022 }
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3.
Li, Z., Wang, X., Hu, D., Robertson, N.M., Clifton, D.A., Meinel, C., Yang, H.: Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge. Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022 (2022).
[ BibTeX ] [ Details ]
 
@inproceedings{li2022not, author = {Li, Ziyun and Wang, Xinshao and Hu, Di and Robertson, Neil M and Clifton, David A and Meinel, Christoph and Yang, Haojin}, booktitle = {Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022}, keywords = {myown}, title = {Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge}, year = 2022 }
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4.
Bartz, C., Raetz, H., Otholt, J., Meinel, C., Yang, H.: Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data. 2022 26th International Conference on Pattern Recognition (ICPR). pp. 3878–3884 (2022).
[ BibTeX ] [ Details ]
 
@inproceedings{9956471, author = {Bartz, Christian and Raetz, Hendrik and Otholt, Jona and Meinel, Christoph and Yang, Haojin}, booktitle = {2022 26th International Conference on Pattern Recognition (ICPR)}, keywords = {myown}, pages = {3878-3884}, title = {Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data}, year = 2022 }
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URNhttp://dx.doi.org/10.1109/ICPR56361.2022.9956471

2021 [ nach oben ]

1.
Guo, N., Bethge, J., Yang, H., Zhong, K., Ning, X., Meinel, C., Wang, Y.: Boolnet: Minimizing the Energy Consumption of Binary Neural Networks. arXiv preprint arXiv:2106.06991. (2021).
[ BibTeX ] [ Details ]
 
@article{guo2021boolnet, author = {Guo, Nianhui and Bethge, Joseph and Yang, Haojin and Zhong, Kai and Ning, Xuefei and Meinel, Christoph and Wang, Yu}, journal = {arXiv preprint arXiv:2106.06991}, keywords = {myown}, title = {Boolnet: Minimizing the Energy Consumption of Binary Neural Networks}, year = 2021 }
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2.
Hilscher, M., Tjabben, H., Rätz, H., Semmo, A., Besanc con, L., Döllner, J., Trapp, M.: Service-based Analysis and Abstraction for Content Moderation of Digital Images. Graphics Interface 2021. , Vancouver, Canada (2021).
[ BibTeX ] [ Details ]
 
@inproceedings{hilscher:hal-03263114, address = {Vancouver, Canada}, author = {Hilscher, Moritz and Tjabben, Hendrik and R{ä}tz, Hendrik and Semmo, Amir and Besan{\c c}on, Lonni and D{ö}llner, J{ü}rgen and Trapp, Matthias}, booktitle = {{Graphics Interface 2021}}, keywords = {myown}, month = {may}, title = {{Service-based Analysis and Abstraction for Content Moderation of Digital Images}}, year = 2021 }
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3.
Von Thienen, J., Borchart, K., Jaschek, C., Krebs, E., Hildebrand, J., Rätz, H., Meinel, C.: Leveraging Video Games to Improve IT-Solutions for Remote Work. 2021 IEEE Conference on Games (CoG). pp. 01–08 (2021).
[ BibTeX ] [ Details ]
 
@inproceedings{9618986, author = {Von Thienen, Julia and Borchart, Kim–Pascal and Jaschek, Corinna and Krebs, Eva and Hildebrand, Justus and Rätz, Hendrik and Meinel, Christoph}, booktitle = {2021 IEEE Conference on Games (CoG)}, keywords = {myown}, pages = {01-08}, title = {Leveraging Video Games to Improve IT-Solutions for Remote Work}, year = 2021 }
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URNhttp://dx.doi.org/10.1109/CoG52621.2021.9618986

4.
Bartz, C., Rätz, H., Meinel, C.: Handwriting Classification for the Analysis of Art-Historical Documents. In: Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., and Vezzani, R. (eds.) Pattern Recognition. ICPR International Workshops and Challenges. pp. 562–576. Springer International Publishing, Cham (2021).
[ Abstract ] [ BibTeX ] [ Details ]
 
Digitized archives contain and preserve the knowledge of generations of scholars in millions of documents. The size of these archives calls for automatic analysis since a manual analysis by specialists is often too expensive. In this paper, we focus on the analysis of handwriting in scanned documents from the art-historic archive of the Wildenstein Plattner Institute. Since the archive consists of documents written in several languages and lacks annotated training data for the creation of recognition models, we propose the task of handwriting classification as a new step for a handwriting OCR pipeline. We propose a handwriting classification model that labels extracted text fragments, e.g., numbers, dates, or words, based on their visual structure. Such a classification supports historians by highlighting documents that contain a specific class of text without the need to read the entire content. To this end, we develop and compare several deep learning-based models for text classification. In extensive experiments, we show the advantages and disadvantages of our proposed approach and discuss possible usage scenarios on a real-world dataset.
@inproceedings{10.1007/978-3-030-68796-0_40, abstract = {Digitized archives contain and preserve the knowledge of generations of scholars in millions of documents. The size of these archives calls for automatic analysis since a manual analysis by specialists is often too expensive. In this paper, we focus on the analysis of handwriting in scanned documents from the art-historic archive of the Wildenstein Plattner Institute. Since the archive consists of documents written in several languages and lacks annotated training data for the creation of recognition models, we propose the task of handwriting classification as a new step for a handwriting OCR pipeline. We propose a handwriting classification model that labels extracted text fragments, e.g., numbers, dates, or words, based on their visual structure. Such a classification supports historians by highlighting documents that contain a specific class of text without the need to read the entire content. To this end, we develop and compare several deep learning-based models for text classification. In extensive experiments, we show the advantages and disadvantages of our proposed approach and discuss possible usage scenarios on a real-world dataset.}, address = {Cham}, author = {Bartz, Christian and R{ä}tz, Hendrik and Meinel, Christoph}, booktitle = {Pattern Recognition. ICPR International Workshops and Challenges}, editor = {Del Bimbo, Alberto and Cucchiara, Rita and Sclaroff, Stan and Farinella, Giovanni Maria and Mei, Tao and Bertini, Marco and Escalante, Hugo Jair and Vezzani, Roberto}, keywords = {myown}, pages = {562–576}, publisher = {Springer International Publishing}, title = {Handwriting Classification for the Analysis of Art-Historical Documents}, year = 2021 }
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HerausgeberDel Bimbo, Alberto and Cucchiara, Rita and Sclaroff, Stan and Farinella, Giovanni Maria and Mei, Tao and Bertini, Marco and Escalante, Hugo Jair and Vezzani, Roberto
AbstractDigitized archives contain and preserve the knowledge of generations of scholars in millions of documents. The size of these archives calls for automatic analysis since a manual analysis by specialists is often too expensive. In this paper, we focus on the analysis of handwriting in scanned documents from the art-historic archive of the Wildenstein Plattner Institute. Since the archive consists of documents written in several languages and lacks annotated training data for the creation of recognition models, we propose the task of handwriting classification as a new step for a handwriting OCR pipeline. We propose a handwriting classification model that labels extracted text fragments, e.g., numbers, dates, or words, based on their visual structure. Such a classification supports historians by highlighting documents that contain a specific class of text without the need to read the entire content. To this end, we develop and compare several deep learning-based models for text classification. In extensive experiments, we show the advantages and disadvantages of our proposed approach and discuss possible usage scenarios on a real-world dataset.

5.
Hu, T., Yang, H., Meinel, C.: Denoising AutoEncoder Based Delete and Generate Approach for Text Style Transfer. International Conference on Artificial Neural Networks. pp. 41–52. Springer, on-line (2021).
[ BibTeX ] [ Details ]
 
@inproceedings{hu2021denoising, address = {on-line}, author = {Hu, Ting and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Artificial Neural Networks}, keywords = {Web University}, month = {sep}, organization = {Springer}, pages = {41–52}, title = {Denoising AutoEncoder Based Delete and Generate Approach for Text Style Transfer}, year = 2021 }
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6.
Bethge, J., Bartz, C., Yang, H., Chen, Y., Meinel, C.: MeliusNet: An Improved Network Architecture for Binary Neural Networks. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2021).
[ BibTeX ] [ Download ] [ Details ]
 
@inproceedings{bethge2021meliusnet, author = {Bethge, Joseph and Bartz, Christian and Yang, Haojin and Chen, Ying and Meinel, Christoph}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, keywords = {myown}, title = {MeliusNet: An Improved Network Architecture for Binary Neural Networks}, year = 2021 }
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7.
Hu, T., Meinel, C.: Masked Hard Coverage Mechanism on Pointer-generator Network for Natural Language Generation. ICAART (2). pp. 1177–1183 (2021).
[ BibTeX ] [ Details ]
 
@inproceedings{hu2021masked, author = {Hu, Ting and Meinel, Christoph}, booktitle = {ICAART (2)}, keywords = {web university}, pages = {1177–1183}, title = {Masked Hard Coverage Mechanism on Pointer-generator Network for Natural Language Generation.}, year = 2021 }
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8.
Yang, H., Shen, Z., Zhao, Y.: AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2339–2348 (2021).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2021asymmnet, author = {Yang, Haojin and Shen, Zhen and Zhao, Yucheng}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, keywords = {myown}, pages = {2339–2348}, title = {AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks}, year = 2021 }
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2020 [ nach oben ]

1.
Bethge, J., Bartz, C., Yang, H., Meinel, C.: BMXNet 2: An Open Source Framework for Low-bit Networks-Reproducing, Understanding, Designing and Showcasing. Proceedings of the 28th ACM International Conference on Multimedia. pp. 4469–4472 (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{bethge2020bmxnet, author = {Bethge, Joseph and Bartz, Christian and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the 28th ACM International Conference on Multimedia}, keywords = {myown}, pages = {4469–4472}, title = {BMXNet 2: An Open Source Framework for Low-bit Networks-Reproducing, Understanding, Designing and Showcasing}, year = 2020 }
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2.
Mordido, G., Yang, H., Meinel, C.: microbatchgan: Stimulating diversity with multi-adversarial discrimination. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3061–3070 (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{mordido2020microbatchgan, author = {Mordido, Gonçalo and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, keywords = {myown}, pages = {3061–3070}, title = {microbatchgan: Stimulating diversity with multi-adversarial discrimination}, year = 2020 }
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3.
Bartz, C., Seidel, L., Nguyen, D.-H., Bethge, J., Yang, H., Meinel, C.: Synthetic Data for the Analysis of Archival Documents: Handwriting Determination. 2020 Digital Image Computing: Techniques and Applications (DICTA). pp. 1–8. IEEE (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{bartz2020synthetic, author = {Bartz, Christian and Seidel, Laurenz and Nguyen, Duy-Hung and Bethge, Joseph and Yang, Haojin and Meinel, Christoph}, booktitle = {2020 Digital Image Computing: Techniques and Applications (DICTA)}, keywords = {myown}, organization = {IEEE}, pages = {1–8}, title = {Synthetic Data for the Analysis of Archival Documents: Handwriting Determination}, year = 2020 }
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4.
Bartz, C., Bethge, J., Yang, H., Meinel, C.: One model to reconstruct them all: A novel way to use the stochastic noise in StyleGAN. The British Machine Vision Conference (BMVC) (2020).
[ BibTeX ] [ Details ]
 
@conference{bartz2020one, author = {Bartz, Christian and Bethge, Joseph and Yang, Haojin and Meinel, Christoph}, booktitle = {The British Machine Vision Conference (BMVC)}, keywords = {myown}, title = {One model to reconstruct them all: A novel way to use the stochastic noise in StyleGAN}, year = 2020 }
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5.
Sauder, J., Hu, T., Che, X., Mordido, G., Yang, H., Meinel, C.: Best student forcing: A simple training mechanism in adversarial language generation. Proceedings of the 12th Language Resources and Evaluation Conference. pp. 4680–4688 (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{sauder2020best, author = {Sauder, Jonathan and Hu, Ting and Che, Xiaoyin and Mordido, Goncalo and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference}, keywords = {myown}, pages = {4680–4688}, title = {Best student forcing: A simple training mechanism in adversarial language generation}, year = 2020 }
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6.
Jain, N., Bartz, C., Bredow, T., Metzenthin, E., Otholt, J., Krestel, R.: Semantic Analysis of Cultural Heritage Data: Aligning Paintings and Descriptions in Art-Historic Collections. International Workshop on Fine Art Pattern Extraction and Recognition in conjunction with the 25th International Conference on Pattern Recognition (ICPR 2020) (2020).
[ Abstract ] [ BibTeX ] [ Details ]
 
Art-historic documents often contain multimodal data in terms of images of artworks and metadata, descriptions, or interpretations thereof. Most research efforts have focused either on image analysis or text analysis independently since the associations between the two modes are usually lost during digitization. In this work, we focus on the task of alignment of images and textual descriptions in art-historic digital collections. To this end, we reproduce an existing approach that learns alignments in a semi-supervised fashion. We identify several challenges while automatically aligning images and texts, specifically for the cultural heritage domain, which limit the scalability of previous works. To improve the performance of alignment, we introduce various enhancements to extend the existing approach that show promising results.
@inproceedings{jain2020semantic, abstract = {Art-historic documents often contain multimodal data in terms of images of artworks and metadata, descriptions, or interpretations thereof. Most research efforts have focused either on image analysis or text analysis independently since the associations between the two modes are usually lost during digitization. In this work, we focus on the task of alignment of images and textual descriptions in art-historic digital collections. To this end, we reproduce an existing approach that learns alignments in a semi-supervised fashion. We identify several challenges while automatically aligning images and texts, specifically for the cultural heritage domain, which limit the scalability of previous works. To improve the performance of alignment, we introduce various enhancements to extend the existing approach that show promising results.}, author = {Jain, Nitisha and Bartz, Christian and Bredow, Tobias and Metzenthin, Emanuel and Otholt, Jona and Krestel, Ralf}, booktitle = {International Workshop on Fine Art Pattern Extraction and Recognition in conjunction with the 25th International Conference on Pattern Recognition (ICPR 2020)}, keywords = {myown}, title = {Semantic Analysis of Cultural Heritage Data: Aligning Paintings and Descriptions in Art-Historic Collections}, year = 2020 }
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AbstractArt-historic documents often contain multimodal data in terms of images of artworks and metadata, descriptions, or interpretations thereof. Most research efforts have focused either on image analysis or text analysis independently since the associations between the two modes are usually lost during digitization. In this work, we focus on the task of alignment of images and textual descriptions in art-historic digital collections. To this end, we reproduce an existing approach that learns alignments in a semi-supervised fashion. We identify several challenges while automatically aligning images and texts, specifically for the cultural heritage domain, which limit the scalability of previous works. To improve the performance of alignment, we introduce various enhancements to extend the existing approach that show promising results.

7.
Rezaei, M., Yang, H., Meinel, C.: Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimedia Tools and Applications. 79, 15329–15348 (2020).
[ BibTeX ] [ Details ]
 
@article{rezaei2020recurrent, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, journal = {Multimedia Tools and Applications}, keywords = {myown}, number = 21, pages = {15329–15348}, publisher = {Springer}, title = {Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation}, volume = 79, year = 2020 }
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8.
Hu, T., Meinel, C.: Text generation in discrete space. International Conference on Artificial Neural Networks. pp. 721–732. Springer (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{hu2020text, author = {Hu, Ting and Meinel, Christoph}, booktitle = {International Conference on Artificial Neural Networks}, keywords = {web university}, organization = {Springer}, pages = {721–732}, title = {Text generation in discrete space}, year = 2020 }
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9.
Hu, T., Meinel, C.: An Investigation of Fine-tuning Pre-trained Model for MR-to-Text Generation. 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). pp. 1006–1009. IEEE (2020).
[ BibTeX ] [ Details ]
 
@inproceedings{hu2020investigation, author = {Hu, Ting and Meinel, Christoph}, booktitle = {2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)}, keywords = {web university}, organization = {IEEE}, pages = {1006–1009}, title = {An Investigation of Fine-tuning Pre-trained Model for MR-to-Text Generation}, year = 2020 }
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2019 [ nach oben ]

1.
Bethge, J., Yang, H., Meinel, C.: Training Accurate Binary Neural Networks from Scratch. 2019 26th IEEE International Conference on Image Processing (ICIP) (2019).
[ Abstract ] [ BibTeX ] [ Details ]
 
Binary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full-precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, we analyze how full-precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state-of-the-art even further. Our source code can be found online: https://github.com/hpi-xnor/BMXNet-v2
@inproceedings{bethge2019training, abstract = {Binary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full-precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, we analyze how full-precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state-of-the-art even further. Our source code can be found online: https://github.com/hpi-xnor/BMXNet-v2}, author = {Bethge, Joseph and Yang, Haojin and Meinel, Christoph}, booktitle = {2019 26th IEEE International Conference on Image Processing (ICIP)}, keywords = {myown}, month = {sep}, title = {Training Accurate Binary Neural Networks from Scratch}, year = 2019 }
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AbstractBinary neural networks are a promising approach to execute convolutional neural networks on devices with low computational power. Previous work on this subject often quantizes pretrained full-precision models and uses complex training strategies. In our work, we focus on increasing the performance of binary neural networks by training from scratch with a simple training strategy. In our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. Further, we analyze how full-precision network structures can be adapted for efficient binary networks and adopt a network architecture based on a DenseNet for binary networks, which lets us improve the state-of-the-art even further. Our source code can be found online: https://github.com/hpi-xnor/BMXNet-v2

2.
Rezaei, M., Yang, H., Harmuth, K., Meinel, C.: Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation. 2019 IEEE winter conference on applications of computer vision (WACV). pp. 1836–1845. IEEE (2019).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2019conditional, author = {Rezaei, Mina and Yang, Haojin and Harmuth, Konstantin and Meinel, Christoph}, booktitle = {2019 IEEE winter conference on applications of computer vision (WACV)}, keywords = {myown}, organization = {IEEE}, pages = {1836–1845}, title = {Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation}, year = 2019 }
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3.
Bethge, J., Yang, H., Bornstein, M., Meinel, C.: BinaryDenseNet: Developing an Architecture for Binary Neural Networks. The IEEE International Conference on Computer Vision (ICCV) Workshops (2019).
[ BibTeX ] [ Details ]
 
@inproceedings{bethge2019binarydensenet, author = {Bethge, Joseph and Yang, Haojin and Bornstein, Marvin and Meinel, Christoph}, booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops}, keywords = {myown}, month = {oct}, title = {BinaryDenseNet: Developing an Architecture for Binary Neural Networks}, year = 2019 }
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2018 [ nach oben ]

1.
Mordido, G., Yang, H., Meinel, C.: Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators. KDD2018 (2018).
[ BibTeX ] [ Details ]
 
@proceedings{mordido2018dropoutgan, author = {Mordido, Gonçalo and Yang, Haojin and Meinel, Christoph}, editor = {Day, ACM KDD'18 Deep Learning}, keywords = {its mml}, organization = {KDD2018}, title = {Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators}, year = 2018 }
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HerausgeberDay, ACM KDD'18 Deep Learning

2.
Bartz, C., Yang, H., Meinel, C.: SEE: towards semi-supervised end-to-end scene text recognition. Proceedings of the AAAI Conference on Artificial Intelligence (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{bartz2018see, author = {Bartz, Christian and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, keywords = {myown}, number = 1, title = {SEE: towards semi-supervised end-to-end scene text recognition}, volume = 32, year = 2018 }
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3.
Bartz, C., Yang, H., Bethge, J., Meinel, C.: Loans: Weakly supervised object detection with localizer assessor networks. Asian Conference on Computer Vision. pp. 341–356. Springer (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{bartz2018loans, author = {Bartz, Christian and Yang, Haojin and Bethge, Joseph and Meinel, Christoph}, booktitle = {Asian Conference on Computer Vision}, keywords = {myown}, organization = {Springer}, pages = {341–356}, title = {Loans: Weakly supervised object detection with localizer assessor networks}, year = 2018 }
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4.
Rezaei, M., Yang, H., Meinel, C.: voxel-GAN: adversarial framework for learning imbalanced brain tumor segmentation. International MICCAI Brainlesion Workshop. pp. 321–333. Springer (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2018voxel, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, booktitle = {International MICCAI Brainlesion Workshop}, keywords = {myown}, organization = {Springer}, pages = {321–333}, title = {voxel-GAN: adversarial framework for learning imbalanced brain tumor segmentation}, year = 2018 }
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5.
Rezaei, M., Yang, H., Meinel, C.: Instance tumor segmentation using multitask convolutional neural network. 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2018instance, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, booktitle = {2018 International Joint Conference on Neural Networks (IJCNN)}, keywords = {myown}, organization = {IEEE}, pages = {1–8}, title = {Instance tumor segmentation using multitask convolutional neural network}, year = 2018 }
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6.
Rezaei, M., Yang, H., Meinel, C.: Whole heart and great vessel segmentation with context-aware of generative adversarial networks. Bildverarbeitung für die Medizin 2018. pp. 353–358. Springer (2018).
[ BibTeX ] [ Details ]
 
@incollection{rezaei2018whole, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, booktitle = {Bildverarbeitung f{ü}r die Medizin 2018}, keywords = {myown}, pages = {353–358}, publisher = {Springer}, title = {Whole heart and great vessel segmentation with context-aware of generative adversarial networks}, year = 2018 }
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2017 [ nach oben ]

1.
Bartz, C., Herold, T., Yang, H., Meinel, C.: Language identification using deep convolutional recurrent neural networks. International conference on neural information processing. pp. 880–889. Springer (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{bartz2017language, author = {Bartz, Christian and Herold, Tom and Yang, Haojin and Meinel, Christoph}, booktitle = {International conference on neural information processing}, keywords = {myown}, organization = {Springer}, pages = {880–889}, title = {Language identification using deep convolutional recurrent neural networks}, year = 2017 }
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2.
Rezaei, M., Yang, H., Meinel, C.: Deep Neural Network with l2-norm Unit for Brain Lesions Detection. International Conference on Neural Information Processing. Springer (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2017, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Neural Information Processing}, keywords = {its mml}, publisher = {Springer}, title = {Deep Neural Network with l2-norm Unit for Brain Lesions Detection}, year = 2017 }
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3.
Yang, H., Fritzsche, M., Bartz, C., Meinel, C.: BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet. Proceedings of the 2017 ACM on Multimedia Conference. ACM, New York, NY, USA (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2017bmxnet, address = {New York, NY, USA}, author = {Yang, Haojin and Fritzsche, Martin and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 2017 ACM on Multimedia Conference}, keywords = {its mml}, publisher = {ACM}, series = {MM '17}, title = {BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet}, year = 2017 }
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4.
Rezaei, M., Harmuth, K., Gierke, W., Kellermeier, T., Fischer, M., Yang, H., Meinel, C.: A conditional adversarial network for semantic segmentation of brain tumor. International MICCAI Brainlesion Workshop. pp. 241–252. Springer (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2017conditional, author = {Rezaei, Mina and Harmuth, Konstantin and Gierke, Willi and Kellermeier, Thomas and Fischer, Martin and Yang, Haojin and Meinel, Christoph}, booktitle = {International MICCAI Brainlesion Workshop}, keywords = {myown}, organization = {Springer}, pages = {241–252}, title = {A conditional adversarial network for semantic segmentation of brain tumor}, year = 2017 }
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5.
Yang, H., Fritzsche, M., Bartz, C., Meinel, C.: Bmxnet: An open-source binary neural network implementation based on mxnet. Proceedings of the 25th ACM international conference on Multimedia (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2017bmxnet, author = {Yang, Haojin and Fritzsche, Martin and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 25th ACM international conference on Multimedia}, keywords = {myown}, title = {Bmxnet: An open-source binary neural network implementation based on mxnet}, year = 2017 }
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6.
Che, X., Ring, N., Raschkowski, W., Yang, H., Meinel, C.: Traversal-free word vector evaluation in analogy space. Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP. pp. 11–15 (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{che2017traversal, author = {Che, Xiaoyin and Ring, Nico and Raschkowski, Willi and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP}, keywords = {myown}, pages = {11–15}, title = {Traversal-free word vector evaluation in analogy space}, year = 2017 }
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7.
Rezaei, M., Yang, H., Meinel, C.: Deep neural network with l2-norm unit for brain lesions detection. International Conference on Neural Information Processing. pp. 798–807. Springer (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{rezaei2017deep, author = {Rezaei, Mina and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Neural Information Processing}, keywords = {myown}, organization = {Springer}, pages = {798–807}, title = {Deep neural network with l2-norm unit for brain lesions detection}, year = 2017 }
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8.
Che, X., Luo, S., Yang, H., Meinel, C.: Automatic lecture subtitle generation and how it helps. 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT). pp. 34–38. IEEE (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{che2017automatic, author = {Che, Xiaoyin and Luo, Sheng and Yang, Haojin and Meinel, Christoph}, booktitle = {2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT)}, keywords = {myown}, organization = {IEEE}, pages = {34–38}, title = {Automatic lecture subtitle generation and how it helps}, year = 2017 }
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9.
Bethge, J., Hahn, S., Döllner, J.: Improving Layout Quality by Mixing Treemap-Layouts Based on Data-Change Characteristics. In: Hullin, M., Klein, R., Schultz, T., and Yao, A. (eds.) Vision, Modeling & Visualization. The Eurographics Association (2017).
[ Abstract ] [ BibTeX ] [ Details ]
 
This paper presents a hybrid treemap layout approach that optimizes layout-quality metrics by combining state-of-the-art treemap layout algorithms. It utilizes machine learning to predict those metrics based on data metrics describing the characteristics and changes of the dataset. For this, the proposed approach uses a neural network which is trained on artificially generated dataset,s containing a total of 15.8 million samples. The resulting model is integrated into an approach called Smart-Layouting. This approach is evaluated on real-world data from 100 publicly available software repositories. Compared to other state-of-the-art treemap algorithms it reaches an overall better result. Additionally, this approach can be customized by an end user’s needs. The customization allows for specifying weights for the importance of each layout-quality metric. The results indicate, that the algorithm is able to adapt successfully towards a given set of weights.
@inproceedings{noauthororeditor, abstract = {This paper presents a hybrid treemap layout approach that optimizes layout-quality metrics by combining state-of-the-art treemap layout algorithms. It utilizes machine learning to predict those metrics based on data metrics describing the characteristics and changes of the dataset. For this, the proposed approach uses a neural network which is trained on artificially generated dataset,s containing a total of 15.8 million samples. The resulting model is integrated into an approach called Smart-Layouting. This approach is evaluated on real-world data from 100 publicly available software repositories. Compared to other state-of-the-art treemap algorithms it reaches an overall better result. Additionally, this approach can be customized by an end user’s needs. The customization allows for specifying weights for the importance of each layout-quality metric. The results indicate, that the algorithm is able to adapt successfully towards a given set of weights.}, author = {Bethge, Joseph and Hahn, Sebastian and Döllner, Jürgen}, booktitle = {Vision, Modeling & Visualization}, editor = {Hullin, Matthias and Klein, Reinhard and Schultz, Thomas and Yao, Angela}, keywords = {myown cgs}, publisher = {The Eurographics Association}, title = {Improving Layout Quality by Mixing Treemap-Layouts Based on Data-Change Characteristics}, type = {Publication}, volume = 2017, year = 2017 }
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HerausgeberHullin, Matthias and Klein, Reinhard and Schultz, Thomas and Yao, Angela
URNhttp://dx.doi.org/10.2312/vmv.20171261
AbstractThis paper presents a hybrid treemap layout approach that optimizes layout-quality metrics by combining state-of-the-art treemap layout algorithms. It utilizes machine learning to predict those metrics based on data metrics describing the characteristics and changes of the dataset. For this, the proposed approach uses a neural network which is trained on artificially generated dataset,s containing a total of 15.8 million samples. The resulting model is integrated into an approach called Smart-Layouting. This approach is evaluated on real-world data from 100 publicly available software repositories. Compared to other state-of-the-art treemap algorithms it reaches an overall better result. Additionally, this approach can be customized by an end user’s needs. The customization allows for specifying weights for the importance of each layout-quality metric. The results indicate, that the algorithm is able to adapt successfully towards a given set of weights.

10.
Hahn, S., Bethge, J., Döllner, J.: Relative Direction Change: A Topology-based Metric for Layout Stability in Treemaps. Proceedings of the 8th International Conference of Information Visualization Theory and Applications (IVAPP 2017) (2017).
[ Abstract ] [ BibTeX ] [ Details ]
 
This paper presents a topology-based metric for layout stability in treemaps—the Relative Direction Change (RDC). The presented metric considers the adjacency and arrangement of single shapes in a treemap, and allows for a rotation-invariant description of layout changes between two snapshots of a dataset depicted with treemaps. A user study was conducted that shows the applicability of the Relative Direction Change in comparison and addition to established layout metrics, such as Average Distance Change (ADC) and Average Aspect Ratio (AAR), with respect to human perception of treemaps. This work contributes to the establishment of a more precise model for the replicable and reliable comparison of treemap layout algorithms © The Authors 2017. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive version will be published in Proceedings of the 8th International Conference on Information Visualization Theory and Applications (IVAPP 2017).
@inproceedings{HBD2017, abstract = {This paper presents a topology-based metric for layout stability in treemaps—the Relative Direction Change (RDC). The presented metric considers the adjacency and arrangement of single shapes in a treemap, and allows for a rotation-invariant description of layout changes between two snapshots of a dataset depicted with treemaps. A user study was conducted that shows the applicability of the Relative Direction Change in comparison and addition to established layout metrics, such as Average Distance Change (ADC) and Average Aspect Ratio (AAR), with respect to human perception of treemaps. This work contributes to the establishment of a more precise model for the replicable and reliable comparison of treemap layout algorithms © The Authors 2017. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive version will be published in Proceedings of the 8th International Conference on Information Visualization Theory and Applications (IVAPP 2017).}, author = {Hahn, Sebastian and Bethge, Joseph and Döllner, Jürgen}, booktitle = {Proceedings of the 8th International Conference of Information Visualization Theory and Applications (IVAPP 2017)}, keywords = {Treemaps myown Layout_Stability Evaluation cgs}, month = 2, title = {Relative Direction Change: A Topology-based Metric for Layout Stability in Treemaps}, year = 2017 }
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AbstractThis paper presents a topology-based metric for layout stability in treemaps—the Relative Direction Change (RDC). The presented metric considers the adjacency and arrangement of single shapes in a treemap, and allows for a rotation-invariant description of layout changes between two snapshots of a dataset depicted with treemaps. A user study was conducted that shows the applicability of the Relative Direction Change in comparison and addition to established layout metrics, such as Average Distance Change (ADC) and Average Aspect Ratio (AAR), with respect to human perception of treemaps. This work contributes to the establishment of a more precise model for the replicable and reliable comparison of treemap layout algorithms © The Authors 2017. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive version will be published in Proceedings of the 8th International Conference on Information Visualization Theory and Applications (IVAPP 2017).

11.
Bartz, C., Herold, T., Yang, H., Meinel, C.: Language Identification Using Deep Convolutional Recurrent Neural Networks. International Conference on Neural Information Processing. Springer (2017).
[ BibTeX ] [ Details ]
 
@inproceedings{bartz2017language, author = {Bartz, Christian and Herold, Tom and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Neural Information Processing}, keywords = {its mml}, publisher = {Springer}, title = {Language Identification Using Deep Convolutional Recurrent Neural Networks}, year = 2017 }
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2016 [ nach oben ]

1.
Che, X., Wang, C., Yang, H., Meinel, C.: Punctuation prediction for unsegmented transcript based on word vector. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). pp. 654–658 (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{che2016punctuation, author = {Che, Xiaoyin and Wang, Cheng and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)}, keywords = {myown its mml}, pages = {654–658}, title = {Punctuation prediction for unsegmented transcript based on word vector}, year = 2016 }
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2.
Wang, C., Yang, H., Bartz, C., Meinel, C.: Image captioning with deep bidirectional LSTMs. Proceedings of the 24th ACM international conference on Multimedia. pp. 988–997 (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{wang2016image, author = {Wang, Cheng and Yang, Haojin and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 24th ACM international conference on Multimedia}, keywords = {myown}, pages = {988–997}, title = {Image captioning with deep bidirectional LSTMs}, year = 2016 }
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3.
Rantzsch, H., Yang, H., Meinel, C.: Signature embedding: Writer independent offline signature verification with deep metric learning. International symposium on visual computing. pp. 616–625. Springer (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{rantzsch2016signature, author = {Rantzsch, Hannes and Yang, Haojin and Meinel, Christoph}, booktitle = {International symposium on visual computing}, keywords = {myown}, organization = {Springer}, pages = {616–625}, title = {Signature embedding: Writer independent offline signature verification with deep metric learning}, year = 2016 }
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4.
Yang, H., Wang, C., Bartz, C., Meinel, C.: SceneTextReg: a real-time video OCR system. Proceedings of the 24th ACM international conference on Multimedia (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2016scenetextreg, author = {Yang, Haojin and Wang, Cheng and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 24th ACM international conference on Multimedia}, keywords = {myown its mml}, title = {SceneTextReg: a real-time video OCR system}, year = 2016 }
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5.
Che, X., Luo, S., Yang, H., Meinel, C.: Sentence Boundary Detection Based on Parallel Lexical and Acoustic Models. Presented at the (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{che2016sentence, author = {Che, Xiaoyin and Luo, Sheng and Yang, Haojin and Meinel, Christoph}, keywords = {myown}, title = {Sentence Boundary Detection Based on Parallel Lexical and Acoustic Models.}, year = 2016 }
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6.
Yang, H., Wang, C., Bartz, C., Meinel, C.: SceneTextReg: A Real-Time Video OCR System. Proceedings of the 2016 ACM on Multimedia Conference. pp. 698–700. ACM, Amsterdam, The Netherlands (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{Yang:2016:SRV:2964284.2973811, address = {New York, NY, USA}, author = {Yang, Haojin and Wang, Cheng and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 2016 ACM on Multimedia Conference}, keywords = {its mml}, pages = {698–700}, publisher = {ACM}, series = {MM '16}, title = {SceneTextReg: A Real-Time Video OCR System}, year = 2016 }
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URNhttp://dx.doi.org/10.1145/2964284.2973811

7.
Wang, C., Yang, H., Bartz, C., Meinel, C.: Image Captioning with Deep Bidirectional LSTMs. Proceedings of the 2016 ACM on Multimedia Conference. pp. 988–997. ACM, Amsterdam, The Netherlands (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{Wang:2016:ICD:2964284.2964299, address = {New York, NY, USA}, author = {Wang, Cheng and Yang, Haojin and Bartz, Christian and Meinel, Christoph}, booktitle = {Proceedings of the 2016 ACM on Multimedia Conference}, keywords = {its mml}, pages = {988–997}, publisher = {ACM}, series = {MM '16}, title = {Image Captioning with Deep Bidirectional LSTMs}, year = 2016 }
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URNhttp://dx.doi.org/10.1145/2964284.2964299

8.
Wang, C., Yang, H., Meinel, C.: Exploring multimodal video representation for action recognition. 2016 International Joint Conference on Neural Networks (IJCNN). pp. 1924–1931 (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{7727435, author = {Wang, Cheng and Yang, Haojin and Meinel, C.}, booktitle = {2016 International Joint Conference on Neural Networks (IJCNN)}, keywords = {its mml}, month = {July}, pages = {1924-1931}, title = {Exploring multimodal video representation for action recognition}, year = 2016 }
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URNhttp://dx.doi.org/10.1109/IJCNN.2016.7727435

9.
Rantzsch, H., Yang, H., Meinel, C.: Signature Embedding: Writer Independent Offline Signature Verification with Deep Metric Learning. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Porikli, F., Skaff, S., Entezari, A., Min, J., Iwai, D., Sadagic, A., Scheidegger, C., and Isenberg, T. (eds.) Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II. pp. 616–625. Springer International Publishing, Cham (2016).
[ Abstract ] [ BibTeX ] [ Details ]
 
The handwritten signature is widely employed and accepted as a proof of a person's identity. In our everyday life, it is often verified manually, yet only casually. As a result, the need for automatic signature verification arises. In this paper, we propose a new approach to the writer independent verification of offline signatures. Our approach, named Signature Embedding, is based on deep metric learning. Comparing triplets of two genuine and one forged signature, our system learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity. Our system ranks best in nearly all evaluation metrics from the ICDAR SigWiComp 2013 challenge. The evaluation shows a high generality of our system: being trained exclusively on Latin script signatures, it outperforms the other systems even for signatures in Japanese script.
@inbook{Rantzsch2016, abstract = {The handwritten signature is widely employed and accepted as a proof of a person's identity. In our everyday life, it is often verified manually, yet only casually. As a result, the need for automatic signature verification arises. In this paper, we propose a new approach to the writer independent verification of offline signatures. Our approach, named Signature Embedding, is based on deep metric learning. Comparing triplets of two genuine and one forged signature, our system learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity. Our system ranks best in nearly all evaluation metrics from the ICDAR SigWiComp 2013 challenge. The evaluation shows a high generality of our system: being trained exclusively on Latin script signatures, it outperforms the other systems even for signatures in Japanese script.}, address = {Cham}, author = {Rantzsch, Hannes and Yang, Haojin and Meinel, Christoph}, booktitle = {Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part II}, editor = {Bebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Porikli, Fatih and Skaff, Sandra and Entezari, Alireza and Min, Jianyuan and Iwai, Daisuke and Sadagic, Amela and Scheidegger, Carlos and Isenberg, Tobias}, keywords = {its mml}, pages = {616–625}, publisher = {Springer International Publishing}, title = {Signature Embedding: Writer Independent Offline Signature Verification with Deep Metric Learning}, year = 2016 }
Weitere Informationen
HerausgeberBebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Porikli, Fatih and Skaff, Sandra and Entezari, Alireza and Min, Jianyuan and Iwai, Daisuke and Sadagic, Amela and Scheidegger, Carlos and Isenberg, Tobias
URNhttp://dx.doi.org/10.1007/978-3-319-50832-0_60
AbstractThe handwritten signature is widely employed and accepted as a proof of a person's identity. In our everyday life, it is often verified manually, yet only casually. As a result, the need for automatic signature verification arises. In this paper, we propose a new approach to the writer independent verification of offline signatures. Our approach, named Signature Embedding, is based on deep metric learning. Comparing triplets of two genuine and one forged signature, our system learns to embed signatures into a high-dimensional space, in which the Euclidean distance functions as a metric of their similarity. Our system ranks best in nearly all evaluation metrics from the ICDAR SigWiComp 2013 challenge. The evaluation shows a high generality of our system: being trained exclusively on Latin script signatures, it outperforms the other systems even for signatures in Japanese script.

10.
Wang, C., Yang, H., Meinel, C.: Exploring multimodal video representation for action recognition. 2016 International Joint Conference on Neural Networks (IJCNN). pp. 1924–1931. IEEE (2016).
[ BibTeX ] [ Details ]
 
@inproceedings{wang2016exploring, author = {Wang, Cheng and Yang, Haojin and Meinel, Christoph}, booktitle = {2016 International Joint Conference on Neural Networks (IJCNN)}, keywords = {myown}, organization = {IEEE}, pages = {1924–1931}, title = {Exploring multimodal video representation for action recognition}, year = 2016 }
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2015 [ nach oben ]

1.
Quehl, B., Yang, H., Sack, H.: Improving text recognition by distinguishing scene and overlay text. , SPIE (2015).
[ BibTeX ] [ Details ]
 
@proceedings{quehl{2015}improving, address = {SPIE}, author = {{Bernhard Quehl} and {Haojin Yang} and {Harald Sack}}, booktitle = {{Seventh International Conference on Machine Vision (ICMV 2014)}}, chapter = {{MV}}, keywords = {its mml}, pages = {{944509}}, title = {{Improving text recognition by distinguishing scene and overlay text}}, volume = {{9445}}, year = {{2015}} }
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2.
Wang, C., Yang, H., Che, X., Meinel, C.: Concept-based multimodal learning for topic generation. International Conference on Multimedia Modeling. pp. 385–395. Springer (2015).
[ BibTeX ] [ Details ]
 
@inproceedings{wang2015concept, author = {Wang, Cheng and Yang, Haojin and Che, Xiaoyin and Meinel, Christoph}, booktitle = {International Conference on Multimedia Modeling}, keywords = {myown}, organization = {Springer}, pages = {385–395}, title = {Concept-based multimodal learning for topic generation}, year = 2015 }
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3.
Luo, S., Yang, H., Meinel, C.: Reward-based Intermittent Reinforcement in Gamification for E-learning. Presented at the (2015).
[ BibTeX ] [ Details ]
 
@inproceedings{luo2015reward, author = {Luo, Sheng and Yang, Haojin and Meinel, Christoph}, keywords = {myown}, title = {Reward-based Intermittent Reinforcement in Gamification for E-learning.}, year = 2015 }
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4.
Yang, H., Wang, C., Che, X., Luo, S., Meinel, C.: An improved system for real-time scene text recognition. Proceedings of the 5th ACM on International Conference on Multimedia Retrieval (2015).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2015improved, author = {Yang, Haojin and Wang, Cheng and Che, Xiaoyin and Luo, Sheng and Meinel, Christoph}, booktitle = {Proceedings of the 5th ACM on International Conference on Multimedia Retrieval}, keywords = {myown its mml}, title = {An improved system for real-time scene text recognition}, year = 2015 }
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5.
Wang, C., Yang, H., Meinel, C.: Deep semantic mapping for cross-modal retrieval. 2015 IEEE 27th International conference on tools with artificial intelligence (ICTAI). pp. 234–241. IEEE (2015).
[ BibTeX ] [ Details ]
 
@inproceedings{wang2015deep, author = {Wang, Cheng and Yang, Haojin and Meinel, Christoph}, booktitle = {2015 IEEE 27th International conference on tools with artificial intelligence (ICTAI)}, keywords = {myown}, organization = {IEEE}, pages = {234–241}, title = {Deep semantic mapping for cross-modal retrieval}, year = 2015 }
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6.
Che, X., Yang, H., Meinel, C.: Adaptive e-lecture video outline extraction based on slides analysis. International Conference on Web-Based Learning. pp. 59–68. Springer (2015).
[ BibTeX ] [ Details ]
 
@inproceedings{che2015adaptive, author = {Che, Xiaoyin and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Web-Based Learning}, keywords = {myown its mml}, organization = {Springer}, pages = {59–68}, title = {Adaptive e-lecture video outline extraction based on slides analysis}, year = 2015 }
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2014 [ nach oben ]

1.
Yang, H., Quehl, B., Sack, H.: A framework for improved video text detection and recognition. Multimedia tools and applications. 69, 217–245 (2014).
[ BibTeX ] [ Details ]
 
@article{yang2014framework, author = {Yang, Haojin and Quehl, Bernhard and Sack, Harald}, journal = {Multimedia tools and applications}, keywords = {myown}, number = 1, pages = {217–245}, publisher = {Springer}, title = {A framework for improved video text detection and recognition}, volume = 69, year = 2014 }
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2013 [ nach oben ]

1.
Yang, H., Grünewald, F., Bauer, M., Meinel, C.: Lecture video browsing using multimodal information resources. International Conference on Web-Based Learning. pp. 204–213. Springer (2013).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2013lecture, author = {Yang, Haojin and Gr{ü}newald, Franka and Bauer, Matthias and Meinel, Christoph}, booktitle = {International Conference on Web-Based Learning}, keywords = {myown}, organization = {Springer}, pages = {204–213}, title = {Lecture video browsing using multimodal information resources}, year = 2013 }
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2.
Che, X., Yang, H., Meinel, C.: Lecture video segmentation by automatically analyzing the synchronized slides. Proceedings of the 21st ACM international conference on Multimedia. pp. 345–348 (2013).
[ BibTeX ] [ Details ]
 
@inproceedings{che2013lecture, author = {Che, Xiaoyin and Yang, Haojin and Meinel, Christoph}, booktitle = {Proceedings of the 21st ACM international conference on Multimedia}, keywords = {myown}, pages = {345–348}, title = {Lecture video segmentation by automatically analyzing the synchronized slides}, year = 2013 }
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3.
Grünewald, F., Yang, H., Meinel, C.: Evaluating the digital manuscript functionality--User testing for lecture video annotation features. International Conference on Web-Based Learning. pp. 214–223. Springer (2013).
[ BibTeX ] [ Details ]
 
@inproceedings{grunewald2013evaluating, author = {Gr{ü}newald, Franka and Yang, Haojin and Meinel, Christoph}, booktitle = {International Conference on Web-Based Learning}, keywords = {myown}, organization = {Springer}, pages = {214–223}, title = {Evaluating the digital manuscript functionality–User testing for lecture video annotation features}, year = 2013 }
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4.
Yang, H., Grünewald, F., Bauer, M., Meinel, C.: Lecture Video Browsing Using Multimodal Information Resources. 12th International Conference on Web-based Learning (ICWL). pp. 204–213. Springer Berlin Heidelberg (2013).
[ BibTeX ] [ Details ]
 
@inproceedings{Haojin2013a, author = {Yang, Haojin and Grünewald, Franka and Bauer, Matthias and Meinel, Christoph}, booktitle = {12th International Conference on Web-based Learning (ICWL)}, keywords = {its mml}, month = 10, number = 8167, pages = {204-213}, publisher = {Springer Berlin Heidelberg}, title = {Lecture Video Browsing Using Multimodal Information Resources}, volume = {Lecture Notes in Computer Science}, year = 2013 }
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URNhttp://dx.doi.org/10.1007/978-3-642-41175-5_21

2012 [ nach oben ]

1.
Yang, H., Gruenewald, F., Meinel, C.: Automated extraction of lecture outlines from lecture videos. Presented at the (2012).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2012automated, author = {Yang, Haojin and Gruenewald, Franka and Meinel, Christoph}, keywords = {myown}, title = {Automated extraction of lecture outlines from lecture videos}, year = 2012 }
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2.
Yang, H., Quehl, B., Sack, H.: Text detection in video images using adaptive edge detection and stroke width verification. 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP). pp. 9–12. IEEE (2012).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2012text, author = {Yang, Haojin and Quehl, Bernhard and Sack, Harald}, booktitle = {2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP)}, keywords = {myown}, organization = {IEEE}, pages = {9–12}, title = {Text detection in video images using adaptive edge detection and stroke width verification}, year = 2012 }
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3.
Yang, H., Oehlke, C., Meinel, C.: An automated analysis and indexing framework for lecture video portal. International Conference on Web-Based Learning. pp. 285–294. Springer (2012).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2012automated, author = {Yang, Haojin and Oehlke, Christoph and Meinel, Christoph}, booktitle = {International Conference on Web-Based Learning}, keywords = {myown}, organization = {Springer}, pages = {285–294}, title = {An automated analysis and indexing framework for lecture video portal}, year = 2012 }
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4.
Hentschel, C., Hercher, J., Knuth, M., Osterhoff, J., Quehl, B., Sack, H., Steinmetz, N., Waitelonis, J., Yang, H.: Open Up Cultural Heritage in Video Archives with Mediaglobe. Proceedings of the 12th International Conference on Innovative Internet Community Services (I2CS 2012) (2012).
[ BibTeX ] [ Details ]
 
@inproceedings{2012_hentschel_mediaglobe, author = {Hentschel, Christian and Hercher, Johannes and Knuth, Magnus and Osterhoff, Johannes and Quehl, Bernhard and Sack, Harald and Steinmetz, Nadine and Waitelonis, Jörg and Yang, Haojin}, booktitle = {Proceedings of the 12th International Conference on Innovative Internet Community Services (I2CS 2012)}, keywords = {its mml}, month = 6, series = {Lecture Notes in Informatics}, title = {Open Up Cultural Heritage in Video Archives with Mediaglobe}, year = 2012 }
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2011 [ nach oben ]

1.
Yang, H., Oehlke, C., Meinel, C.: German speech recognition: A solution for the analysis and processing of lecture recordings. 2011 10th IEEE/ACIS International Conference on Computer and Information Science. pp. 201–206. IEEE (2011).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2011german, author = {Yang, Haojin and Oehlke, Christoph and Meinel, Christoph}, booktitle = {2011 10th IEEE/ACIS International Conference on Computer and Information Science}, keywords = {mml}, organization = {IEEE}, pages = {201–206}, title = {German speech recognition: A solution for the analysis and processing of lecture recordings}, year = 2011 }
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2.
Yang, H., Siebert, M., Luhne, P., Sack, H., Meinel, C.: Lecture video indexing and analysis using video ocr technology. 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems. pp. 54–61. IEEE (2011).
[ BibTeX ] [ Details ]
 
@inproceedings{yang2011lecture, author = {Yang, Haojin and Siebert, Maria and Luhne, Patrick and Sack, Harald and Meinel, Christoph}, booktitle = {2011 Seventh International Conference on Signal Image Technology \& Internet-Based Systems}, keywords = {myown}, organization = {IEEE}, pages = {54–61}, title = {Lecture video indexing and analysis using video ocr technology}, year = 2011 }
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