is well-understood in vision science that in the primate visual cortex, the sizes of the local receptive fields of neurons in the same region are different [@hubel1962receptive; @riesenhuber1999hierarchical; @serre2007robust; @hung2005fast]. Therefore, such a mechanism of collecting multi-scale spatial information in the same layer needs to be incorporated in CNNs [@huang2017multi; @hrnet; @fourure2017residual; @Szegedy2015]. In this paper, we propose the multi-scale residual block (MRB), as shown in Fig. \[fig:framework\]. It is capable of generating a spatially-precise output by maintaining high-resolution representations, while receiving rich contextual information from low-resolutions. The MRB consists of multiple (three in this paper) fully-convolutional streams connected in parallel. It allows information exchange across parallel streams in order to consolidate the high-resolution features with the help of low-resolution features, and vice versa. Next, we describe individual components of MRB.

**Selective kernel feature fusion (SKFF).** One fundamental property of neurons present in the visual cortex is to be able to change their receptive fields according to the stimulus [@li2019selective]. This mechanism of adaptively adjusting receptive fields can be incorporated in CNNs by using multi-scale feature generation (in the same layer) followed by feature aggregation and selection. The most commonly used approaches for feature aggregation include simple concatenation or summation. 