R-CNN(Regions with CNN features)
本篇论文是目标检测的开山之作，之后的大部分文章都是基于本篇论文进行修改和优化得到的产物。 之前的目标检测算法是穷举搜索（ExhaustiveSearch）：使用一个窗口在图片上进行滑动，改变窗口的大小，继续扫描整张图像。通过寻找响应值最高的那个位置作为预测的目标。而本文是首先从图像中提取可能是物体的区域，然后对这些区域进行判断。这样的做的优点是过滤掉了一些无用的box ,节省了时间。 主要流程
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- 1. Learning Transferable Features with Deep Adaptation Networks
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- 5. 翻译“Learning Transferable Features with Deep Adaptation Networks”
- 6. zoom-out: Feedforward semantic segmentation with zoom-out features
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相关文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. Language Identification with Deep Bottleneck Features
- 3. Faster R-CNN Features for Instance Search
- 4. Content-based image retrieval with compact deep convolutional features
- 5. 翻译“Learning Transferable Features with Deep Adaptation Networks”
- 6. zoom-out: Feedforward semantic segmentation with zoom-out features
- 7. 2D Features framework7(Feature Matching with FLANN )
- 8. Regions with Convolutional NeuralNetwork Features (RCNN) 学习笔记
- 9. Visual saliency detection based on multiscale deep cnn features
- 10. [zoom-out]Feedforward semantic segmentation with zoom-out features