qq_37194898的博客

代码地址：https://github.com/RuoyiDu/PMG-Progressive-Multi-Granularity-TrainingMotivation:less effort has been placed to which granularities are the most discriminative and how to fuse information cross multi-granularity.关键技术点：(i) a progressive training st

内容:It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community.作为预训练模型有更好的精确率和更快的收敛速度！介绍数据集的标注过程这样更清晰！最大的亮点就是对数据集迁移能力的分析：Iterations所谓iterations就是完成一次epoch所需的batch个数。刚.

数据集整体概述The IP102 datset contains more than 75,000 images belongs to 102 categories. A natural long-tailed distribution presents on it. In addition, we annotate 19,000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy a

文章目录论文信息一、特征金字塔二、本文论文的内容1.引入库2.读入数据总结论文信息一、特征金字塔在介绍这篇文章前，首先需要了解一下特征金字塔：这篇论文是CVPR2017年的文章，采用特征金字塔代替了传统的Scale handling 方法论文链接：https://arxiv.org/pdf/1612.03144.pdfhttps://blog.youkuaiyun.com/xiamentingtao/article/details/78598027https://github.com/kuangli

Resolution Adaptive Networks for Efficient InferenceMotivationMethodExperiments借鉴意义与思考论文地址：http://arxiv.org/abs/2003.07326论文代码：http://github.com/yangle15/RANet-pytorchMotivation深度CNN带来了性能提升的同时也带来了过高的计算量，许多研究放在了如何进行网络加速上面，其中比较直接的是根据样本难易程度进行自动调整的自适应网络。基

《FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding》这篇论文来自港中文的林达华团队，是今年CVPR2020的满分论文，特此写博客分析一下。论文地址：https://arxiv.org/abs/2004.06704项目地址：https://sdolivia.github.io/FineGym/...