# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 1 Introduction  
In recent years, the field of computer vision has undergone tremendous changes, with deep learning becoming a powerful tool. Owing to its data-driven nature and the availability of massively parallel computing, deep neural networks have achieved state-of-the-art results in most areas, and researchers have designed many advanced network architectures [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. The ImageNet competition champion [PERSON] [8] was the first to apply convolutional neural networks (CNNs) to a deep network. Subsequently, more deep neural network architectures have been proposed, such as VGGNet [1], GoogLeNet [2], ResNet [6], WideResNet [7], ResNeXt [9], Xception [10], MobileNet [11], and DenseNet [12]. Many other visual recognition algorithms [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] have been inspired by these designs, notably [1; 2; 6; 8]. With improvements in detection accuracy and real-time performance, the object detection algorithm based on deep learning has gradually developed into two types: the two-stage approach and one-stage approach. The one-stage approach [19; 20; 21; 22] incorporates training and detection in a network and solves object detection as a regression problem. Compared with the two-stage approach [13; 16; 17; 18], the one-stage approach has a better real-time performance while maintaining better detection accuracy. In addition to deep learning and object detection methods, previous research has studied the importance of attention [23; 24; 25]. We focus on the interaction between feature-selective and spatial attention and the impact on receptive fields (RFs). The classical RF (CRF) of neurons in the V1 region was discussed in [26]. Researchers have proposed that in such a visual zone as the V4 region, attention should be paid to the effects of neuronal discharge rates in two ways [27]. One is the input gating model. In this model, the RF of the mediators in the theory corresponds to the stimulus that is noted or ignored in the field of view. Another theory is the neuronal strobing model, which states that neurons in the V4 region themselves have enhanced and inhibited effects on attention. In addition, [PERSON] et al. [28] found that stimulation outside the CRF also affected neuronal responses. [29; 30; 31; 32] report important interactions between feature-selective and spatial attention, and each type of attention enhances the effect of another type of attention. Moreover, SKNet [33] propose Selective Kernel Networks with a Selective Kernel convolution, to aggregate information from multiple kernels to realize the adaptive RF sizes of neurons in a nonlinear approach. However, SKNet [33] may be insufficient to provide neurons with powerful adaptation ability. In this paper, we propose a novel architectural unit called a \"Feature-selective and Spatial Receptive Fields\" (FSRF) block that implements adaptive RF sizes of neurons through the additive effects of feature-selective and spatial attention. To achieve this, we use a set of operators in the FSRF block: Multi-branch Convolution, Fuse, and Interactions between Feature-selective and Spatial Attention. The Multi-branch Convolution generates multiple paths corresponding to different RF sizes. The Fuse operator combines information from multiple paths to obtain a global weight representation. The Interactions between Feature-selective and Spatial Attention operator aggregates feature maps of different RF sizes according to the additive effects of feature-selective and spatial attention. The structure of the FSRF block is simple and can be used directly in the state-of-the-art architectures currently available.