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ICCV (IEEE/CVF International Conference on Computer Vision)

  • Gao, B., Gouk, H. and Hospedales, T.M., 2021. Searching for robustness: Loss learning for noisy classification tasks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6670-6679). [ www ] ( CMA-ES )
    • "We use the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to solve the upper layer problem, and standard stochastic gradient-based optimisation approaches to solve the lower level problems."
      • Nikolaus Hansen and Andreas Ostermeier. Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In CEC, 1996.
      • Santiago Gonzalez and Risto Miikkulainen. Improved training speed, accuracy, and data utilization through loss function optimization. In CEC, 2020.
      • Santiago Gonzalez and Risto Miikkulainen. Optimizing loss functions through multi-variate taylor polynomial parameterization. In GECCO, 2021.
      • Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le. Regularized evolution for image classifier architecture search. In AAAI, 2019.
  • Piergiovanni, A.J., Angelova, A., Toshev, A. and Ryoo, M.S., 2019. Evolving space-time neural architectures for videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 1793-1802). [ www | pdf | Python ]
  • Shu, H., Wang, Y., Jia, X., Han, K., Chen, H., Xu, C., Tian, Q. and Xu, C., 2019. Co-evolutionary compression for unpaired image translation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3235-3244). [ www | pdf ]
  • Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K.T. and Sun, J., 2019. Metapruning: Meta learning for automatic neural network channel pruning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3296-3305). [ www | pdf | Python ]
  • Halber, M., Shi, Y., Xu, K. and Funkhouser, T., 2019. Rescan: Inductive instance segmentation for indoor rgbd scans. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2541-2550). [ www | pdf | project | C ] (Simulated Annealing)
  • Chen, Y., Huang, S., Yuan, T., Qi, S., Zhu, Y. and Zhu, S.C., 2019. Holistic++ scene understanding: Single-view 3d holistic scene parsing and human pose estimation with human-object interaction and physical commonsense. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8648-8657). [ www | pdf | project | Python ] (Simulated Annealing)
  • Xie, L. and Yuille, A., 2017. Genetic cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1379-1388). [ www | pdf ] (Distributed Genetic Algorithm | NeuroEvolution)
  • Arad, B. and Ben-Shahar, O., 2017. Filter selection for hyperspectral estimation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3153-3161). [ www | pdf ]
  • Qi, S., Huang, S., Wei, P. and Zhu, S.C., 2017. Predicting human activities using stochastic grammar. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1164-1172). [ www | pdf ] (Simulated Annealing)
  • Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K. and Shotton, J., 2015. Opening the black box: Hierarchical sampling optimization for estimating human hand pose. In Proceedings of IEEE International Conference on Computer Vision (pp. 3325-3333). [ www | pdf ] ( PSO | Continuous Optimization )
  • Meyer, G.P., Gupta, S., Frosio, I., Reddy, D. and Kautz, J., 2015. Robust model-based 3d head pose estimation. In Proceedings of IEEE International Conference on Computer Vision (pp. 3649-3657). [ www | pdf ] ( PSO | Continuous Optimization )