# Low-Resolution Target Detection with High-Frequency Information Preservation
## 1 Introduction  
Despite great advancements in target (e.g., object, human body joints) detection from unconstrained images [1, 2, 3, 4, 5, 6], high-resolution (HR) visual observations are often assumed. However, in practice, there are a large number of low-resolution (LR) targets with much less visual detail available, and their detection performance is still significantly lagging behind due to their lack of a fine-grained, high-frequency appearance [2, 7, 8, 9] (see Figure 1). This challenge is further amplified by the typical downsampling operations (e.g., stride, pooling) of common deep learning models (e.g., CNNs) for computational cost management and receptive field expansion [2, 10, 11].  
Among a number of recent task-specific attempts at solving the LR challenge [2, 8, 12, 13], [PERSON] et al. [2] proposed a model generic SPD-Conv operation, serving as a replacement for conventional downsampling operations.  
However, SPD-Conv still suffers from several inherent limitations:* Through interval sampling, its space-to-depth step could disrupt the spatial structure and information continuity of the original LR feature maps. This would inevitably cause a loss in high-frequency information during the sampling process.
* By inheriting the intrinsic defects of convolution operations, its feature fusion prioritizes low-frequency information whilst suppressing high-frequency information [14].  
To address both limitations given above, in this paper, we propose a novel generic _High-Frequency Information Preservation_ (HFIP) block. HFIP features a frequency-based information process whilst focusing on preserving scarce high-frequency observations during spatial downsampling. The block consists of three units, as shown in Figure 2: the dilated frequency-aware channel correlation (DFACC) unit, the decoupled high-frequency learning (DHFL) unit, and the linear fusion (LF) unit. In particular, the DHFL unit decomposes the spatial information along the horizontal and vertical directions and extracts the respective high-frequency features. Meanwhile, the DFACC unit equips the space-to-depth operation with frequency-based channel correlation. In addition, we find that high-frequency information is mostly present in shallow layers. We thus restrict the corresponding operations in the stem of the network model for efficiency.  
In summary, our contributions are listed as follows:  
* We reveal that the high-frequency information process is a significant component in dealing with low-resolution target detection.
* We introduce a generic high-frequency information preservation (HFIP) block to improve the performance of existing detection models in the LR case. For efficiency, shallow layers are selected for the high-frequency process.
* Extensive experiments on LR human pose estimation and object detection tasks show that our HFIP method can consistently enhance the performance of existing detection models.  
Figure 1: The importance of tackling information loss, especially high-frequency information, for low-resolution tasks, using object detection as an example. The baseline is the original model, e.g., YOLOF [1]. SPD [2] aims to solve the information loss problem. The proposed HFIP method enhances high-frequency information encoding while also mitigating overall information loss, to address low-resolution challenges.