You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Inference. As illustrated in Figure 2, the feature maps shared between RPN and R-FCN are computed (on an image with a single scale of 600). Then the RPN part proposes RoIs, on which the R-FCN part evaluates category-wise scores and regresses bounding boxes.During inference we evaluate 300 RoIs as in [18] for fair comparisons. The results are post-processed by non-maximum suppression (NMS) using a threshold of 0.3 IoU [7], as standard practice.

Question: What's the reason for sharing features between R-FCN and RPN
The feature maps contain information from the input image. The RPN uses those features to find RoIs, while the R-FCN uses those features to detect objects.