@article{Li_Chen_Yu_Deng_2018, title={R-FCN++: Towards Accurate Region-Based Fully Convolutional Networks for Object Detection}, volume={32}, url={https://ojs.aaai.org/index.php/AAAI/article/view/12265}, DOI={10.1609/aaai.v32i1.12265}, abstractNote={ <p> Region based detectors like Faster R-CNN and R-FCN have achieved leading performance on object detection benchmarks. However, in Faster R-CNN, RoI pooling is used to extract feature of each region, which might harm the classification as the RoI pooling loses spatial resolution. Also it gets slow when a large number of proposals are utilized. R-FCN is a fully convolutional structure that uses a position-sensitive pooling layer to extract prediction score of each region, which speeds up network by sharing computation of RoIs and prevents the feature map from losing information in RoI-pooling. But R-FCN can not benefit from fully connected layer (or global average pooling), which enables Faster R-CNN to utilize global context information. In this paper, we propose R-FCN++ to address this issue in two-fold: first we involve Global Context Module to improve the classification score maps by adopting large, separable convolutional kernels. Second we introduce a new pooling method to better extract scores from the score maps, by using row-wise or column-wise max pooling. Our approach achieves state-of-the-art single-model results on both Pascal VOC and MS COCO object detection benchmarks, 87.3% on Pascal VOC 2012 test dataset and 42.3% on COCO 2015 test-dev dataset. Code will be made publicly available. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Zeming and Chen, Yilun and Yu, Gang and Deng, Yangdong}, year={2018}, month={Apr.} }