The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
OpenPSS: An Open Page Stream Segmentation Benchmark
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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Adaptive document image binarization
Sauvola, J., & Pietikäinen, M. (2000) · Pattern Recognition · doi:10.1016/s0031-3203(99)00055-2 · Cited by 2,339
Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659
Deep multimodal learning: a survey on recent advances and trends
Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139 · cited 1×
“In early ensembling, the final layers of two models are combined before the final classification is made, and a prediction is obtained by adding a final classification layer on top of this combined output to obtain the final prediction [20].”
A theoretical study on six classifier fusion strategies
Kuncheva, L. I. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982906 · Cited by 720
Statistical Models for Text Segmentation
Beeferman, D., Berger, A., & Lafferty, J. (1999) · Machine Learning · doi:10.1023/a:1007506220214 · Cited by 680
Combining Artificial Neural Nets
SHARKEY, A. J. C. (1999) · Perspectives in neural computing · doi:10.1007/978-1-4471-0793-4 · Cited by 306
Building a test collection for complex document information processing
Lewis, D., Agam, G., Argamon, S., Frieder, O., Grossman, D., & Heard, J. (2006) · Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1148170.1148307 · Cited by 286
Text Segmentation as a Supervised Learning Task
Koshorek, O., Cohen, A., Mor, N., Rotman, M., & Berant, J. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2075 · Cited by 167
Research works printed but not linked: 3
- Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st Meeting of the North American Chapter of the Association for Computational Linguistics (2000). https://aclanthology.org/A00-2004
- Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the International Conference on Machine Learning, pp. 1188–1196. PMLR (2014)
- Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 6105–6114. PMLR (2019)
Metadata
- Authors
- Heusden, R. V., Kamps, J., & Marx, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Pages
- 413-429
- ISSN
- 0302-9743
- 1611-3349
- Cited by
- 3
- References
- 24
- Language
- en
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- CoChat. (2026). Citation check: OpenPSS: An Open Page Stream Segmentation Benchmark. https://app.cochat.ai/papers/10.1007/978-3-031-72437-4_24
- The paper
- Heusden, R. V., Kamps, J., & Marx, M. (2024). OpenPSS: An Open Page Stream Segmentation Benchmark. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-72437-4_24
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
