King, D.E.: Max-Margin Object Detection. CoRR abs/1502.00046 (2015)King Davis E. Max-margin object detection arXiv preprint arXiv:1502.00046. 2015.D. E. King, "Max-margin object detection", CoRR, vol. ab
novel类的表示和分类之间存在着矛盾:一方面来说,为了实现准确地将novel类分类,两个base类的分布必须彼此远离(Max-margin);另一方面,为了精确地表示novel类,base类的分布应该彼此接近,以减少novel类的类内距离(Min-margin)。 如何在分类和表示中取得平衡,是一个问题。 创新点 提出了一种类边界均衡(CME)方法,旨在系...
Quad Tolerance:Sets the highest acceptable margin of error from the Face Count vs. the actual retopologized mesh. Note that lower values will increase computation time. Subdivision:Specifies The ratio of surface points skipped vs those sampled by retopologization. Skipping many points (ie. choosin...
文【3】中提出了Large-Margin Softmax Loss,简称为L-Softmax loss。 我们先从一张图来理解下softmax loss,这张图显示的是不同softmax loss和L-Softmax loss学习到的cnn特征分布。第一列就是softmax,第2列是L-Softmax loss在参数m取不同值时的分布。通过可视化特征可知学习到的类间的特征是比较明显的,但是...
--scaf-margin Margin hyperparameter for sub-center ArcFace loss --backbone-checkpoint Path to checkpoint from which to load backbone weights Display and statistics --enable-tensorboard Enable logging to TensorBoard (default: disabled) --confusion Display the confusion matrix --param-hist Collec...
Compared to MobileNet, ShuffleNet achieves superior performance by a significant margin due to it's efficient structure. Top-1 error from paper - ~32.6% ShuffleNet_V2 Zhang et al. Extremely computation efficient CNN model that is designed specifically for mobile devices. This network architecture...
这部分可以参考损失函数改进之Large-Margin Softmax Loss,基本一样。 公式6也就是作者文中最终使用的loss公式。因此A-softmax loss可以看成是large margin softmax loss的改进版,也就是在large margin softmax loss的基础上添加了两个限制条件。 Figure3表示从几何角度看A-softmax loss。
“Learning structured prediction models: A large margin approach.” Proceedings of the 22nd international conference on Machine learning. ACM, 2005. Ben Taskar Carlos Guestrin Daphne Koller. “Max-margin Markov networks.” Advances in neural information processing systems 16 (2004): 25. Barnard, ...
Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for ...
Code Choose a Transformer: Fourier or Galerkin 1 code implementation•NeurIPS 2021 Without softmax, the approximation capacity of a linearized Transformer variant can be proved to be comparable to a Petrov-Galerkin projection layer-wise, and the estimate is independent with respect to the sequence ...