Most object detectors contain two important components: a feature extractor and an object classifier. The feature extractor has rapidly evolved with significant research efforts leading to better deep ConvNet architectures. The object classifier, however, has not received much attention and most state-of-the-art systems (like R-CNN) use simple multi-layer perceptrons. This paper demonstrates that carefully designing deep networks for object classification is just as important. We take inspiration from traditional object classifiers, such as DPM, and experiment with deep networks that have part-like filters and reason over latent variables. We discover that on pre-trained convolutional feature maps, even randomly initialized deep classifiers produce excellent results, while the improvement due to fine-tuning is secondary; on HOG features, deep classifiers outperform DPMs and produce the best HOG-only results without external data. We believe these findings provide new insight for developing object detection systems. Our framework, called Networks on Convolutional feature maps (NoC), achieves outstanding results on the PASCAL VOC 2007 (73.3% mAP) and 2012 (68.8% mAP) benchmarks.

本论文探讨了如何使用共享的、独立于区域的卷积特征构建区域分类器网络，并发现深度和卷积的区域分类器对于物体检测特别重要，而最新的图像分类模型则不会直接导致良好的检测准确性，我们通过实验证明，尽管ResNets和Faster R-CNN系统是有效的，但NoCs的设计对于ImageNet和MS COCO 2015挑战赛的第一名入选至关重要。

卷积特征图上的目标检测网络