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: In contrast, a one-stage detector must process a much larger set of candidate object locations regularly sampled across an image. In practice this often amounts to enumerating \scriptstyle\sim100k locations that densely cover spatial positions, scales, and aspect ratios. While similar sampling heuristics may also be applied, they are inefficient as the training procedure is still dominated by easily classified background examples. This inefficiency is a classic problem in object detection that is typically addressed via techniques such as bootstrapping [33, 29] or hard example mining [37, 8, 31].

Question: One stage detectors are computationally inexpensive than two stage detectors. Is it true?
From the above evidential sentence, we can see that a one-stage detector has to process larger set of candidate object locations regularly sampled across an image. So, we can say that one stage detectors are computationally expensive than two stage detectors. Thus, the answer is False.