Deep Learning-Based Concept Detection in vitrivr

1. Abadi, M., Barham, P., Chen, J., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI), vol. 16, pp. 265–283. USENIX, Savannah, GA, USA (2016)

   Google Scholar 

2. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany (2018, page to appear)

   Google Scholar 

3. Cobârzan, C., et al.: Interactive video search tools: a detailed analysis of the video browser showdown 2015. Multimedia Tools and Appl. (MTAP) 76(4), 5539–5571 (2017)

   Google Scholar 

4. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223. IEEE, Las Vegas (2016)

   Google Scholar 

5. Mark Everingham, S.M., Eslami, A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. (IJCV) 111(1), 98–136 (2015)

   Article  Google Scholar 

6. Furuta, R., Inoue, N., Yamasaki, T.: Efficient and interactive spatial-semantic image retrieval. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10704, pp. 190–202. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73603-7_16

   Chapter  Google Scholar 

7. Giangreco, I., Schuldt, H.: ADAM\(_{pro}\): database support for big multimedia retrieval. Datenbank-Spektrum 16(1), 17–26 (2016)

   Google Scholar 

8. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227, pp. 1–10 (2014)

9. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Li, F.-F.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732. IEEE, Columbus (2014)

   Google Scholar 

10. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

11. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. (JMLR) 9(Nov), 2579–2605 (2008)

    MATH  Google Scholar 

12. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, pp. 1–12 (2013)

13. Rossetto, L., Giangreco, I., Gasser, R., Schuldt, H.: Competitive video retrieval with vitrivr. In: Schoeffmann, K., et al. (eds.) MMM 2018. LNCS, vol. 10705, pp. 403–406. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73600-6_41

    Chapter  Google Scholar 

14. Rossetto, L., et al.: IMOTION – searching for video sequences using multi-shot sketch queries. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 377–382. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27674-8_36

    Chapter  Google Scholar 

15. Rossetto, L., Giangreco, I., Schuldt, H.: Cineast: a multi-feature sketch-based video retrieval engine. In: Proceedings of the International Symposium on Multimedia (ISM), pp. 18–23. IEEE, Taichung, December 2014

    Google Scholar 

16. Rossetto, L., et al.: IMOTION — a content-based video retrieval engine. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8936, pp. 255–260. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14442-9_24

    Chapter  Google Scholar 

17. Rossetto, L., Giangreco, I., Tănase, C., Schuldt, H.: vitrivr: a flexible retrieval stack supporting multiple query modesfor searching in multimedia collections. In: Proceedings of the ACM Conference on Multimedia Conference (ACM MM), pp. 1183–1186. ACM, Amsterdam, October 2016

    Google Scholar 

18. Rossetto, L., Giangreco, I., Tănase, C., Schuldt, H., Dupont, S., Seddati, O.: Enhanced retrieval and browsing in the IMOTION system. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 469–474. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51814-5_43

    Chapter  Google Scholar 

19. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 39(11), 2298–2304 (2017)

    Article  Google Scholar 

20. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 4489–4497. IEEE, Santiago (2015)

    Google Scholar 

21. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 39(4), 652–663 (2017)

    Article  Google Scholar 

22. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 4. IEEE, Honolulu (2017)

    Google Scholar 

23. Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2642–2651. IEEE, Honolulu (2017)

    Google Scholar 

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