Dr. E. (Stratis) Gavves
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 904
Postadres
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Postbus 94323
1090 GH Amsterdam
Contactgegevens
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Publicaties
- refereed (86)
- wetenschappelijk (1)
- vakpublicatie (1)
- congresbijdragen (6)
- supervised theses (7)
- proefschrift (1)
- datasets (5)
2023
- Auzina, I. A., Yildiz, C., Magliacane, S., Bethge, M., & Gavves, E. (2023). Modulated Neural ODEs. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper_files/paper/2023/hash/8bc74514d554a90c996576f6c373f5f3-Abstract-Conference.html
Bondesan, R., Gavves, E., Oh, C., & Welling, M. (2023). Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 10, pp. 6843-6858). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/2d779258dd899505b56f237de66ae470-Abstract-Conference.html [details]- Knigge, D. M., Romero, D. W., Gu, A., Bekkers, E. J., Gavves, E., Tomczak, J. M., Hoogendoorn, M., & Sonke, J. J. (2023). Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN. In International Conference on Learning Representations
- Knigge, D. M., Romero, D. W., Gu, A., Gavves, E., Bekkers, E. J., Tomczak, J. M., Hoogendoorn, M., & Sonke, J. (2023). The Continuous CNN: from Task-Specific to Unified CNN Architecture. In International Conference on Learning Representations https://openreview.net/forum?id=ZW5aK4yCRqU
- Kofinas, M., Bekkers, E. J., Nagaraja, N. S., & Gavves, E. (in press). Latent Field Discovery in Interacting Dynamical Systems with Neural Fields. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation.
- Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. In The Eleventh International Conference on Learning Representations https://openreview.net/forum?id=itZ6ggvMnzS
- Liu, Y., Magliacane, S., Kofinas, M., & Gavves, E. (2023). Graph switching dynamical systems. In International Conference on Machine Learning
- Mirzaei, H., Salehidehnavi, S., Shahabi, S., Gavves, E., Snoek, C. G. M., Sabokrou, M., & Rohban, M. (2023). Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection. In International Conference on Learning Representations
- Pervez, A., Lippe, P., & Gavves, E. (2023). Differentiable Mathematical Programming for Object-Centric Representation Learning. In International Conference on Learning Representations https://openreview.net/forum?id=1J-ZTr7aypY
- Pervez, A., Lippe, P., & Gavves, E. (2023). Scalable Subset Sampling with Neural Conditional Poisson Networks. In International Conference on Learning Representations https://openreview.net/forum?id=p8hMBcPtvju
- Salehi, M., Gavves, E., Snoek, C. G. M., & Asano, Y. M. (2023). Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations. In 2023 IEEE/CVF International Conference on Computer Vision: ICCV 2023 : Paris, France, 2-6 October 2023 : proceedings (pp. 16490-16501). IEEE Computer Society. https://doi.org/10.1109/ICCV51070.2023.01516, https://doi.org/10.48550/arXiv.2308.11796 [details]
- Trémuel, P. G., Gavves, E., Würsch, C., Frick, K., & Vetsch, R. (2023). Parameter-free Neural Field-based Optimal Design of Nonuniform Transmission Lines. In ICECS 2023: 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS) : 4-7 December 2023, Hilton Maslak İstanbul, Turkey (pp. 211-214). IEEE. https://doi.org/10.1109/ICECS58634.2023.10382765 [details]
- Wang, H., Yan, C., Wang, S., Jiang, X., Tang, X., Hu, Y., Xie, W., & Gavves, E. (2023). Towards Open-Vocabulary Video Instance Segmentation. In 2023 IEEE/CVF International Conference on Computer Vision: ICCV 2023 : Paris, France, 2-6 October 2023 : proceedings (pp. 4034-4043). IEEE Computer Society. https://doi.org/10.48550/arXiv.2304.01715, https://doi.org/10.1109/ICCV51070.2023.00375 [details]
- Yin, W., Sonke, J. J., & Gavves, E. (2023). PC-Reg: A pyramidal prediction–correction approach for large deformation image registration. Medical Image Analysis, 90, Article 102978. https://doi.org/10.1016/j.media.2023.102978 [details]
2022
- Bereska, L., & Gavves, E. (2022). Continual Learning of Dynamical Systems with Competitive Federated Reservoir Computing. Proceedings of Machine Learning Research, 199, 335-350. https://doi.org/10.48550/arXiv.2206.13336 [details]
- Chen, Y., Fernando, B., Bilen, H., Nießner, M., & Gavves, E. (2022). 3D Equivariant Graph Implicit Functions. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.), Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022 : proceedings (Vol. III, pp. 485–502). (Lecture Notes in Computer Science; Vol. 13663). Springer. Advance online publication. https://doi.org/10.48550/arXiv.2203.17178, https://doi.org/10.1007/978-3-031-20062-5_28 [details]
- Gavves, E., Kofinas, M., & Nagaraja, N. (2022). Roto-translated Local Coordinate Frames For Interacting Dynamical Systems. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 8, pp. 6417-6429). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/32b991e5d77ad140559ffb95522992d0-Abstract.html [details]
- Habibian, A., Ben Yahia, H., Abati, D., Gavves, E., & Porikli, F. (2022). Delta Distillation for Efficient Video Processing. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.), Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022 : proceedings (Vol. XXXV, pp. 213-229). (Lecture Notes in Computer Science; Vol. 13695). Springer. https://doi.org/10.1007/978-3-031-19833-5_13 [details]
- Lippe, P., Cohen, T., & Gavves, S. (2022). Efficient Neural Causal Discovery without Acyclicity Constraints. In International Conference on Learning Representations https://openreview.net/forum?id=eYciPrLuUhG
- Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2022). CITRIS: Causal Identifiability from Temporal Intervened Sequences. Proceedings of Machine Learning Research, 162, 13557-13603. https://proceedings.mlr.press/v162/lippe22a.html [details]
- Liu, J., Bao, Y., Xie, G-S., Xiong, H., Sonke, J-J., & Gavves, E. (2022). Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: New Orleans, Louisiana, 19-24 June 2022 : proceedings (pp. 11543-11552). (CVPR). IEEE Computer Society. https://doi.org/10.48550/arXiv.2204.10638, https://doi.org/10.1109/CVPR52688.2022.01126 [details]
- Pervez, A. A., & Gavves, E. (2022). Stability Regularization for Discrete Representation Learning. In International Conference on Learning Representations
- Schirris, Y., Engelaer, M., Panteli, A., Horlings, H. M., Gavves, E., & Teuwen, J. (2022). WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need. In J. E. Tomaszewski, A. D. Ward, & R. M. Levenson (Eds.), Medical Imaging 2022: Digital and Computational Pathology: 21-27 March 2022, online Article 120390B (Proceedings of SPIE; Vol. 12039), (Progress in Biomedical Optics and Imaging; Vol. 23, No. 55). SPIE. https://doi.org/10.1117/12.2611528 [details]
- Schirris, Y., Gavves, E., Nederlof, I., Horlings, H. M., & Teuwen, J. (2022). DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer. Medical Image Analysis, 79, Article 102464. Advance online publication. https://doi.org/10.1016/j.media.2022.102464 [details]
- Wang, H., Liu, J., Liu, Y., Maji, S., Sonke, J-J., & Gavves, E. (2022). Dynamic Transformer for Few-shot Instance Segmentation. In MM '22: proceedings of the 30th ACM International Conference on Multimedia : October 10-14, 2022, Lisboa, Portugal (pp. 2969–2977). The Association for Computing Machinery. https://doi.org/10.1145/3503161.3548227 [details]
- Wang, H., Shen, J., Liu, Y., Gao, Y., & Gavves, E. (2022). NFormer: Robust Person Re-identification with Neighbor Transformer. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: New Orleans, Louisiana, 19-24 June 2022 : proceedings (pp. 7287-7297). (CVPR). IEEE Computer Society. https://doi.org/10.48550/arXiv.2204.09331, https://doi.org/10.1109/CVPR52688.2022.00715 [details]
- Zoetmulder, R., Bruggeman, A. A. E., Išgum, I., Gavves, E., Majoie, C. B. L. M., Beenen, L. F. M., Dippel, D. W. J., Boodt, N., den Hartog, S. J., van Doormaal, P. J., Cornelissen, S. A. P., Roos, Y. B. W. E. M., Brouwer, J., Schonewille, W. J., Pirson, A. F. V., van Zwam, W. H., van der Leij, C., Brans, R. J. B., van Es, A. C. G. M., ... MR CLEAN Registry Investigators (2022). Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke. Diagnostics, 12(6), Article 1400. https://doi.org/10.3390/diagnostics12061400 [details]
- Zoetmulder, R., Gavves, E., Caan, M., & Marquering, H. (2022). Domain- and Task-Specific Transfer Learning For Medical Segmentation Tasks. Computer Methods and Programs in Biomedicine, 214, Article 106539. Advance online publication. https://doi.org/10.1016/j.cmpb.2021.106539 [details]
2021
- Chen, Y., Fernando, B., Bilen, H., Mensink, T., & Gavves, E. (2021). Neural Feature Matching in Implicit 3D Representations. Proceedings of Machine Learning Research, 139, 1582-1593. https://proceedings.mlr.press/v139/chen21f.html [details]
- Gavves, E., Tao, R., Gupta, D. K., & Smeulders, A. W. M. (2021). Model Decay in Long-Term Tracking. In Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition : Milan, 10-15 January 2021 (pp. 2685-2692). IEEE. https://doi.org/10.1109/ICPR48806.2021.9412648 [details]
- Gupta, D. K., Arya, D., & Gavves, E. (2021). Rotation Equivariant Siamese Networks for Tracking. In Proceedings, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition: virtual, 9-25 June 2021 (pp. 12357-12366). (CVPR). Conference Publishing Services, IEEE Computer Society. https://doi.org/10.48550/arXiv.2012.13078, https://doi.org/10.1109/CVPR46437.2021.01218 [details]
- Gupta, D. K., Gavves, E., & Smeulders, A. W. M. (2021). Tackling Occlusion in Siamese Tracking with Structured Dropouts. In Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition : Milan, 10-15 January 2021 (pp. 5804-5811). IEEE. https://doi.org/10.1109/ICPR48806.2021.9412120 [details]
- Kilickaya, M., Hussein, N., Gavves, E., & Smeulders, A. (2021). Self-Selective Context for Interaction Recognition. In Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition : Milan, 10-15 January 2021 (pp. 2280-2287). IEEE. https://doi.org/10.1109/ICPR48806.2021.9413326 [details]
- Liao, S., Gavves, E., Oh, C., & Snoek, C. G. M. (2021). Quasibinary Classifier for Images with Zero and Multiple Labels. In Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition : Milan, 10-15 January 2021 (pp. 8743-8750). IEEE. https://doi.org/10.1109/ICPR48806.2021.9412933 [details]
- Lippe, P., & Gavves, E. (2021). Categorical Normalizing Flows via Continuous Transformations. In International Conference on Learning Representations https://openreview.net/pdf?id=-GLNZeVDuik
- Panteli, A., Teuwen, J., Horlings, H., & Gavves, E. (2021). Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations. In 2021 IEEE/CVF International Conference on Computer Vision: proceedings : ICCV 2021 : 11-17 October 2021, virtual event (pp. 2793-2803). (International Conference on Computer Vision; Vol. 18). IEEE Computer Society. https://doi.org/10.1109/ICCV48922.2021.00281 [details]
- Pervez, A., & Gavves, E. (2021). Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders. Proceedings of Machine Learning Research, 139, 8536-8545. https://proceedings.mlr.press/v139/pervez21a.html [details]
- Shi, Z., Chen, Y., Gavves, E., Mettes, P., & Snoek, C. G. M. (2021). Unsharp Mask Guided Filtering. IEEE Transactions on Image Processing, 30, 7472-7485. Advance online publication. https://doi.org/10.1109/TIP.2021.3106812 [details]
- Zoetmulder, R., Konduri, P. R., Obdeijn, I. V., Gavves, E., Išgum, I., Majoie, C. B. L. M., Dippel, D. W. J., Roos, Y. B. W. E. M., Goyal, M., Mitchell, P. J., Campbell, B. C. V., Lopes, D. K., Reimann, G., Jovin, T. G., Saver, J. L., Muir, K. W., White, P., Bracard, S., Chen, B., ... Marquering, H. A. (2021). Automated final lesion segmentation in posterior circulation acute ischemic stroke using deep learning. Diagnostics, 11(9), Article 1621. https://doi.org/10.3390/diagnostics11091621 [details]
2020
- Chen, Y., Hu, V. T., Gavves, E., Mensink, T., Mettes, P., Yang, P., & Snoek, C. G. M. (2020). PointMixup: Augmentation for Point Clouds. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings (Vol. III, pp. 330-345). (Lecture Notes in Computer Science; Vol. 12348). Springer. https://doi.org/10.1007/978-3-030-58580-8_20 [details]
- Oh, C., Tomczak, J. M., Gavves, E., & Welling, M. (2020). Combinatorial Bayesian Optimization using the Graph Cartesian Product. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 4, pp. 2891-2901). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/2cb6b10338a7fc4117a80da24b582060-Abstract.html [details]
- Panteli, A., Gupta, D. K., de Bruijn, N., & Gavves, E. (2020). Siamese Tracking of Cell Behaviour Patterns. Proceedings of Machine Learning Research, 121, 570-587. http://proceedings.mlr.press/v121/panteli20a.html [details]
- Pervez, A., Cohen, T., & Gavves, E. (2020). Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. Proceedings of Machine Learning Research, 119, 7632-7640. http://proceedings.mlr.press/v119/pervez20a.html [details]
- van de Leur, R. R., Blom, L. J., Gavves, E., Hof, I. E., van der Heijden, J. F., Clappers, N. C., Doevendans, P. A., Hassink, R. J., & van Es, R. (2020). Automatic Triage of 12‐Lead ECGs Using Deep Convolutional Neural Networks. Journal of the American Heart Association, 9(10). https://doi.org/10.1161/JAHA.119.015138 [details]
2019
- Chen, Y., Mensink, T., & Gavves, E. (2019). 3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation. In 2019 International Conference on 3D Vision: 3DV 2019 : proceedings : Quebec, Canada, 15-18 September 2019 (pp. 173-182). IEEE Computer Society, Conference Publishing Services. https://doi.org/10.48550/arXiv.1910.01460, https://doi.org/10.1109/3DV.2019.00028 [details]
- Hussein, N., Gavves, E., & Smeulders, A. W. M. (2019). Timeception for Complex Action Recognition. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition: proceedings : 16-20 June 2019, Long Beach, California (pp. 254-263). (CVPR). IEEE Computer Society. https://doi.org/10.1109/CVPR.2019.00034 [details]
- Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Pflugfelder, R., Kämäräinen, J-K., Čehovin Zajc, L., Drbohlav, O., Lukežič, A., Berg, A., Eldesokey, A., Käpylä, J., Fernández, G., Gonzalez-Garcia, A., Memarmoghadam, A., Lu, A., He, A., Varfolomieiev, A., Chan, A., ... Ni, Z. (2019). The Seventh Visual Object Tracking VOT2019 Challenge Results. In 2019 International Conference on Computer Vision, Workshops: proceedings : 27 October-2 November 2019, Seoul, Korea (pp. 2206-2241). IEEE Computer Society. https://doi.org/10.1109/ICCVW.2019.00276 [details]
- Liao, S., Gavves, E., & Snoek, C. G. M. (2019). Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition: proceedings : 16-20 June 2019, Long Beach, California (pp. 9751-9759). (CVPR). IEEE Computer Society. https://doi.org/10.48550/arXiv.1904.05404, https://doi.org/10.1109/CVPR.2019.00999 [details]
- Louizos, C., Reisser, M., Blankevoort, T., Gavves, E., & Welling, M. (2019). Relaxed Quantization for Discretized Neural Networks. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=HkxjYoCqKX [details]
- O'Connor, P., Gavves, E., & Welling, M. (2019). Initialized Equilibrium Propagation for Backprop-Free Training. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. Advance online publication. https://openreview.net/forum?id=B1GMDsR5tm [details]
- O'Connor, P., Gavves, E., & Welling, M. (2019). Training a Spiking Neural Network with Equilibrium Propagation. Proceedings of Machine Learning Research, 89, 1516-1523. http://proceedings.mlr.press/v89/o-connor19a.html [details]
- Samson, L., van Noord, N., Booij, O., Hofmann, M., Gavves, E., & Ghafoorian, M. (2019). I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation. In 2019 International Conference on Computer Vision, Workshops: proceedings : 27 October-2 November 2019, Seoul, Korea (pp. 951-960). IEEE Computer Society. https://doi.org/10.1109/ICCVW.2019.00124 [details]
- Shkodrani, S., Hofmann, M., & Gavves, E. (2019). Dynamic Adaptation on Non-Stationary Visual Domains. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018 : proceedings (Vol. II, pp. 158-171). (Lecture Notes in Computer Science; Vol. 11130). Springer. https://doi.org/10.1007/978-3-030-11012-3_12 [details]
- van der Heiden, T., Nagaraja, N. S., Weiss, C., & Gavves, E. (2019). SafeCritic: Collision-Aware Trajectory Prediction. In British Machine Vision Conference Workshop https://arxiv.org/abs/1910.06673
2018
- Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2018). Action recognition with dynamic image networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2799-2813. Advance online publication. https://doi.org/10.1109/TPAMI.2017.2769085 [details]
- Georgoulis, S., Rematas, K., Ritschel, T., Gavves, E., Fritz, M., Van Gool, L., & Tuytelaars, T. (2018). Reflectance and natural illumination from single-material specular objects using deep learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8), 1932-1947. Advance online publication. https://doi.org/10.1109/TPAMI.2017.2742999 [details]
- Ghodrati, A., Gavves, E., & Snoek, C. G. M. (2018). Video Time: Properties, Encoders and Evaluation. In British Machine Vision Conference 2018: BMVC 2018, Newcastle, UK, September 3-6, 2018 Article 859 BMVA Press. [details]
- Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., & Snoek, C. G. M. (2018). VideoLSTM Convolves, Attends and Flows for Action Recognition. Computer Vision and Image Understanding, 166, 41-50. Advance online publication. https://doi.org/10.1016/j.cviu.2017.10.011 [details]
- Liao, S., Gavves, E., & Snoek, C. G. M. (2018). Searching and Matching Texture-free 3D Shapes in Images. In ICMR'18: proceedings of the 2018 ACM International Conference on Multimedia Retrieval : June 11-14, 2018, Yokohama, Japan (pp. 326-334). The Association for Computing Machinery. https://doi.org/10.1145/3206025.3206057 [details]
- O'Connor, P. E., Gavves, E., & Welling, M. (2018). Initialized Equilibrium Propagation for Backprop-Free Training. In International Conference on Machine Learning: Workshop on Credit Assignment in Deep Learning and Deep Reinforcement Learning
- O'Connor, P. E., Gavves, E., & Welling, M. (2018). Temporally Efficient Deep Learning with Spikes. In International Conference on Learning Representations OpenReview. https://openreview.net/forum?id=HkZy-bW0-¬eId=HkZy-bW0-
- Oh, C., Gavves, E., & Welling, M. (2018). BOCK: Bayesian Optimization with Cylindrical Kernels. Proceedings of Machine Learning Research, 80, 3868-3877. http://proceedings.mlr.press/v80/oh18a.html [details]
- Scheepers, T., Kanoulas, E., & Gavves, E. (2018). Improving Word Embedding Compositionality using Lexicographic Definitions. In The Web Conference 2018: companion of the World Wide Web Conference WWW2018 : April 23-27, 2018, Lyon, France (pp. 1083-1093). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3178876.3186007 [details]
- Valmadre, J., Bertinetto, L., Henriques, J. F., Tao, R., Vedaldi, A., Smeulders, A. W. M., Torr, P. H. S., & Gavves, E. (2018). Long-Term Tracking in the Wild: A Benchmark. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018: proceedings (Vol. III, pp. 692-707). (Lecture Notes in Computer Science; Vol. 11207). Springer. https://doi.org/10.1007/978-3-030-01219-9_41 [details]
2017
- Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2017). Self-Supervised Video Representation Learning With Odd-One-Out Networks. In 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings (pp. 5729-5738). IEEE. https://doi.org/10.1109/CVPR.2017.607 [details]
- Fernando, B., Gavves, E., Oramas M., J., Ghodrati, A., & Tuytelaars, T. (2017). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 773-787. Advance online publication. https://doi.org/10.1109/TPAMI.2016.2558148 [details]
- Hussein, N., Gavves, E., & Smeulders, A. W. M. (2017). Unified Embedding and Metric Learning for Zero-Exemplar Event Detection. In 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings (pp. 2087-2096). IEEE. https://doi.org/10.1109/CVPR.2017.225 [details]
- Li, Z., Tao, R., Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2017). Tracking by Natural Language Specification. In 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings (pp. 7350-7358). IEEE. https://doi.org/10.1109/CVPR.2017.777 [details]
2016
- Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic Image Networks for Action Recognition. In Proceedings 29th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2016: 26 June-1 July 2016, Las Vegas, Nevada (pp. 3034-3042). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.331 [details]
- De Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., & Tuytelaars, T. (2016). Online Action Detection. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016 : proceedings (Vol. 5, pp. 269-284). (Lecture Notes in Computer Science; Vol. 9909). Springer. https://doi.org/10.1007/978-3-319-46454-1_17 [details]
- Rematas, K., Ritschel, T., Fritz, M., Gavves, E., & Tuytelaars, T. (2016). Deep Reflectance Maps. In Proceedings 29th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2016: 26 June-1 July 2016, Las Vegas, Nevada (pp. 4508-4516). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.488 [details]
- Tao, R., Gavves, E., & Smeulders, A. W. M. (2016). Siamese Instance Search for Tracking. In Proceedings 29th IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2016: 26 June-1 July 2016, Las Vegas, Nevada (pp. 1420-1429 ). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.158 [details]
2015
- Gavves, E., Fernando, B., Snoek, C. G. M., Smeulders, A. W. M., & Tuytelaars, T. (2015). Local Alignments for Fine-Grained Categorization. International Journal of Computer Vision, 111(2), 191-212. Advance online publication. https://doi.org/10.1007/s11263-014-0741-5 [details]
- Gavves, E., Mensink, T., Tommasi, T., Snoek, C. G. M., & Tuytelaars, T. (2015). Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks. In Proceedings: 2015 IEEE International Conference on Computer Vision: 11-18 December 2015, Santiago, Chile (pp. 2731-2739). IEEE Computer Society. https://doi.org/10.1109/ICCV.2015.313 [details]
2014
- Li, Z., Gavves, E., Mensink, T., & Snoek, C. G. M. (2014). Attributes Make Sense on Segmented Objects. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014: proceedings (Vol. VI, pp. 350-365). (Lecture Notes in Computer Science; Vol. 8694). Springer. https://doi.org/10.1007/978-3-319-10599-4_23 [details]
- Mazloom, M., Gavves, E., & Snoek, C. G. M. (2014). Conceptlets: Selective Semantics for Classifying Video Events. IEEE Transactions on Multimedia, 16(8), 2214-2228. https://doi.org/10.1109/TMM.2014.2359771 [details]
- Mensink, T., Gavves, E., & Snoek, C. G. M. (2014). COSTA: Co-Occurrence Statistics for Zero-Shot Classification. In Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition: 23-28 June 2014, Columbus, Ohio (pp. 2441-2448). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.313 [details]
- Tao, R., Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2014). Locality in Generic Instance Search from One Example. In Proceedings: 2014 IEEE Conference on Computer Vision and Pattern Recognition: 23-28 June 2014, Columbus, Ohio (pp. 2099-2106). IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.269 [details]
2013
- Gavves, E., Fernando, B., Snoek, C. G. M., Smeulders, A. W. M., & Tuytelaars, T. (2013). Fine-Grained Categorization by Alignments. In 2013 IEEE International Conference on Computer Vision: ICCV 2013 : proceedings: 1-8 December 2013, Sydney, NSW, Australia (pp. 1713-1720). IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.215 [details]
- Li, Z., Gavves, E., van de Sande, K. E. A., Snoek, C. G. M., & Smeulders, A. W. M. (2013). Codemaps: Segment, Classify and Search Objects Locally. In 2013 IEEE International Conference on Computer Vision: ICCV 2013 : proceedings: 1-8 December 2013, Sydney, NSW, Australia (pp. 2136-2143). IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.454 [details]
- Mazloom, M., Gavves, E., van de Sande, K. E. A., & Snoek, C. G. M. (2013). Searching Informative Concept Banks for Video Event Detection. In ICMR'13: proceedings of the third ACM International Conference on Multimedia Retrieval : April 16-20, 2013, Dallas, Texas, USA (pp. 255-262). Association for Computing Machinery. https://doi.org/10.1145/2461466.2461507 [details]
2012
- Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2012). Convex reduction of high-dimensional kernels for visual classification. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 3610-3617). IEEE. https://doi.org/10.1109/CVPR.2012.6248106 [details]
- Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2012). Visual synonyms for landmark image retrieval. Computer Vision and Image Understanding, 116(2), 238-249. https://doi.org/10.1016/j.cviu.2011.10.004 [details]
2011
- Li, X., Gavves, E., Snoek, C. G. M., Worring, M., & Smeulders, A. W. M. (2011). Personalizing automated image annotation using cross-entropy. In MM '11: proceedings of the 2011 ACM Multimedia Conference & Co-Located Workshops: Nov. 28-Dec. 1, 2011, Scottsdale, AZ, USA (pp. 233-242). Association for Computing Machinery. https://doi.org/10.1145/2072298.2072330 [details]
2010
- Gavves, E., & Snoek, C. G. M. (2010). Landmark Image Retrieval Using Visual Synonyms. In MM '10: proceedings of the ACM Multimedia 2010 International Conference: October 25-29, 2010, Firenze, Italy (pp. 1123-1126). Association for Computing Machinery. https://doi.org/10.1145/1873951.1874166 [details]
- Snoek, C. G. M., van de Sande, K. E. A., de Rooij, O., Huurnink, B., Gavves, E., Odijk, D., de Rijke, M., Gevers, T., Worring, M., Koelma, D. C., & Smeulders, A. W. M. (2010). The MediaMill TRECVID 2010 semantic video search engine. In TRECVID 2010 notebook National Institute of Standards and Technology. http://www-nlpir.nist.gov/projects/tvpubs/tv10.papers/mediamill.pdf [details]
2023
- Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., & Gavves, E. (2023). BISCUIT: Causal Representation Learning from Binary Interactions. Proceedings of Machine Learning Research, 216, 1263-1273. https://proceedings.mlr.press/v216/lippe23a.html [details]
2017
- Tao, R., Gavves, E., & Smeulders, A. W. M. (2017). Generic mapping for tracking target object in video sequence. (Patent No. US Patent App. 15/192,935). https://patents.google.com/patent/US20170132472A1/en
2021
- Lippe, P., & Gavves, E. (2021). Categorical Normalizing Flows via Continuous Transformations. Paper presented at 9th International Conference on Learning Representations, virtual. https://openreview.net/pdf?id=-GLNZeVDuik
2019
- Davidson, T. R., Tomczak, J. M., & Gavves, E. (2019). Increasing Expressivity of a Hyperspherical VAE. Paper presented at Bayesian Deep Learning Workshop, Vancouver, British Columbia, Canada. https://doi.org/10.48550/arXiv.1910.02912 [details]
- Gupta, D. K., de Bruijn, N., Panteli, A., & Gavves, E. (2019). Tracking-Assisted Segmentation of Biological Cells. Paper presented at Medical Imaging meets NeurIPS workshop 2019, Vancouver, British Columbia, Canada. [details]
- Hussein, N., Gavves, E., & Smeulders, A. W. M. (2019). VideoGraph: Recognizing Minutes-Long Human Activities in Videos. Paper presented at 1st Workshop on Graph Based Learning in Computer Vision, Seoul, Korea, Republic of. https://arxiv.org/abs/1905.05143 [details]
2017
- Mironenco, M., Kianfar, D., Tran, M. K., Kanoulas, E., & Gavves, E. (2017). Examining Cooperation in Visual Dialog Models.
2016
- Snoek, C. G. M., Dong, J., Li, X., Wei, Q., Wang, X., Lan, W., Gavves, E., Hussein, N., Koelma, D. C., & Smeulders, A. W. M. (2016). University of Amsterdam and Renmin University at TRECVID 2016: Searching Video, Detecting Events and Describing Video. Paper presented at TRECVID workshop 2016, Gaithersburg, Maryland, United States. https://www-nlpir.nist.gov/projects/tvpubs/tv16.papers/mediamill.pdf [details]
2023
- Chen, Y. (2023). Continuity in 3D visual learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Oh, C. (2023). Bayesian optimization on non-conventional search spaces. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Pervez, A. A. (2023). Structural constraints in neural network representations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Zoetmulder, R. (2023). Deep-learning-based image segmentation for uncommon ischemic stroke: From infants to adults. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2021
2024
- Zhang, Y., Snoek, C. G. M., Kofinas, M., Knyazev, B., Chen, Y., Burghouts, G. J. & Gavves, S. (8-5-2024). CNN Wild Park - Graph Neural Networks for Learning Equivariant Representations of Neural Networks. Zenodo. https://doi.org/10.5281/zenodo.12797219
2023
- Papa, S., Valperga, R., Knigge, D., Kofinas, M., Lippe, P., Sonke, J.-J. & Gavves, S. (15-12-2023). Neural Field Arena - Classification. Zenodo. https://doi.org/10.5281/zenodo.10392793
- Kofinas, M., Bekkers, E., Nagaraja, N. & Gavves, S. (13-12-2023). Dynamic gravitational field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields. Zenodo. https://doi.org/10.5281/zenodo.10634923
- Kofinas, M., Bekkers, E., Nagaraja, N. & Gavves, S. (13-12-2023). Electrostatic field dataset - Latent Field Discovery in Interacting Dynamical Systems with Neural Fields. Zenodo. https://doi.org/10.5281/zenodo.10631646
2021
- Kofinas, M., Nagaraja, N. & Gavves, S. (9-12-2021). 3D Charged Particles Dataset - Roto-translated Local Coordinate Frames for Interacting Dynamical Systems. Zenodo. https://doi.org/10.5281/zenodo.7500066
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Bondesan, R., Gavves, E., Oh, C., & Welling, M. (2023). Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 10, pp. 6843-6858). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation.