Input scale plays an important role in modern
detection frameworks, and an optimal training scale for images
exists empirically. However, the optimal one usually cannot be
reached in facing extremely large images under the memory
constraint. In this study, we explore the scale effect inside the
object detection pipeline and find that feature upsampling with
the introduction of high-resolution information benefits the detection.
Compared with direct input upscaling, feature upsampling
trades a small performance loss for a large amount of memory
savings. From these observations, we propose a self-supervised
feature augmentation network, which takes downsampled images
as inputs and aims to generate comparable features with the
ones when feeding upscaled images to networks. We present a
guided feature upsampling module, which takes downsampled
images as inputs, to learn upscaled feature representations with
the supervision of real large features acquired from upscaled
images. In a self-supervised learning manner, we can introduce
detailed information of images to the network. For an efficient
feature upsampling, we design a residualized sub-pixel convolution
block based on a sub-pixel convolution layer, which involves
considerable information in upsampling process. Experiments on
Mapillary Vistas Dataset (MVD), Cityscapes, and COCO are
conducted to demonstrate the effectiveness of our method. On the
MVD and Cityscapes detection benchmarks, in which the images
are extremely large, our method surpasses current approaches.
On COCO, the proposed method obtains comparable results to
existing methods but with higher efficiency.