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CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition)

  • Goudreault, F., Scheuble, D., Bijelic, M., Robidoux, N. and Heide, F., 2023. LiDAR-in-the-loop hyperparameter optimization. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13404-13414). [ pdf | Python ] ( CMA-ES )
    • "We optimize sensing and DSP hyperparameters by solving a Multi-Objective black-box Optimization (MOO) problem with a novel CMA-ES (Covariance Matrix Adaptation-Evolution Strategy) that relies on a maxrank multi-objective scalarization loss to dynamically improve scale matching between different loss components."
  • Tian, S., Cai, Y., Yu, H.X., Zakharov, S., Liu, K., Gaidon, A., Li, Y. and Wu, J., 2023. Multi-object manipulation via object-centric neural scattering functions. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9021-9031). [ pdf ] ( CMA-ES + Continuous Optimization #)
    • "To reduce computational cost, we decrease the search space for inverse parameter estimation at this step by reducing the initial standard deviation of CMA compared to the initial step. We also alleviate the assumption of object masks past the initial frame, and compute the loss for the CMA optimizer as the mean-squared error (MSE) between rendered and observed RGB frames."
      • Nikolaus Hansen, Sibylle D. Muller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cmaes). Evolutionary Computation, 11(1):1–18, 2003.
      • Masashi. Shibata. Cma-es. https://github.com/CyberAgentAILab/cmaes, 2022.
  • Nguyen, A., Yosinski, J. and Clune, J., 2015. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 427-436). [ www | pdf ]
  • Kim, S.W., Zhou, Y., Philion, J., Torralba, A. and Fidler, S., 2020. Learning to simulate dynamic environments with gamegan. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1231-1240). [ www | pdf | Python ] (CMA-ES)
  • Li, S., Ke, L., Pratama, K., Tai, Y.W., Tang, C.K. and Cheng, K.T., 2020. Cascaded deep monocular 3D human pose estimation with evolutionary training data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6173-6183). [ www | pdf | Python ]
  • Lu, Z., Deb, K. and Boddeti, V.N., 2020. Muxconv: Information multiplexing in convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12044-12053). [ www | pdf | Python ]
  • Kyriazis, N. and Argyros, A., 2013. Physically plausible 3d scene tracking: The single actor hypothesis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9-16). [ www | pdf ]
  • Qian, C., Sun, X., Wei, Y., Tang, X. and Sun, J., 2014. Realtime and robust hand tracking from depth. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1106-1113). [ www | pdf ] ( PSO | Continuous Optimization )
  • Oikonomidis, I., Lourakis, M.I. and Argyros, A.A., 2014. Evolutionary quasi-random search for hand articulations tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 3422-3429). [ www | pdf ] ( PSO | Continuous Optimization )
  • Oikonomidis, I., Kyriazis, N. and Argyros, A.A., 2012, June. Tracking the articulated motion of two strongly interacting hands. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1862-1869). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Li, H., Shen, T. and Huang, X., 2009, June. Global optimization for alignment of generalized shapes. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 856-863). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Zhang, X., Hu, W., Maybank, S., Li, X. and Zhu, M., 2008, June. Sequential particle swarm optimization for visual tracking. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Liebelt, J. and Schertler, K., 2007, June. Precise registration of 3D models to images by swarming particles. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Padeleris, P., Zabulis, X. and Argyros, A.A., 2012, June. Head pose estimation on depth data based on particle swarm optimization. In IEEE International Conference on Computer Vision Workshops (pp. 42-49). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Thida, M., Remagnino, P. and Eng, H.L., 2009, September. A particle swarm optimization approach for multi-objects tracking in crowded scene. In IEEE International Conference on Computer Vision Workshops (pp. 1209-1215). IEEE. [ www ] ( PSO | Continuous Optimization )
  • Owechko, Y., Medasani, S. and Srinivasa, N., 2004, June. Classifier swarms for human detection in infrared imagery. In IEEE International Conference on Computer Vision Workshops (pp. 121-121). IEEE. [ www ] ( PSO | Continuous Optimization )