## 3 Proposed Attention-Guided Encoder-Decoder Convolutional Neural Network (AED-CNN)
### Decoder Module  
In the encoder module, the low-level layers contain high spatial resolution but lack enough semantic features. By contrast, the high-level layers have rich semantic features but low spatial resolution. It is difficult to extract discriminative features from the feature layers of encoder module to detect pedestrians in low-resolution IR images. In [18], the deconvolutional network has been proposed to refine the traditional feedforward convolutional network, which shows that the deconvolutional module equalizes the representability of each feature map of a sophisticated network and makes the network more efficient. Therefore, to include more high-level context in detection, we add a decoder module at the end of the encoder to effectively make an encoder-decoder hourglass network structure. The decoder module consists of a series of deconvolution layers with successively increasing resolution of feature map layers, namely deconvolution layer 1 (D1) to deconvolution layer 4 (D4), as shown by the red blocks in Figure 1. In [18], five deconvolution layers are used from the top-most feature maps, in which all fine details are missing. Moreover, the additional deconvolution layers result in more computational cost, making it impractical for real-time applications because of the large inference time. To overcome this, we make our decoder shallower and only four layers are used to maintain a higher detection speed.  
Skip Connection Block  
As mentioned above, the encoder-decoder module is exploited to obtain feature maps with rich context information via combining the high-level semantic information with the low-level detailed  
Figure 3: Attention net.  
information. To this end, a new skip connection block, as shown in Figure 2, is introduced to fuse the feature maps from the decoder and the corresponding feature maps from the encoder. In order to share the structure of skip connection block, the deep layers are symmetrically connected with these shallow layers. In other words, the deep features have the same down sampling factor. Specifically, feature maps in the decoder are up sampled and then merged with the feature maps in the decoder. Figure 2 shows the proposed skip connection block. First, the feature map in the encoder is up sampled by a \(2\times 2\) deconvolution layer to match the size of the feature map in the decoder. The feature map in the decoder is followed by a \(3\times 3\) convolution layer, a ReLU layer, and a \(3\times 3\) convolution layer. We merge them by using the element-wise sum. Then, the summed layer passes through a block (ReLU, \(3\times 3\) convolution, ReLU), which is useful for discernable feature extraction as proven in [19], and then summed with the deconvolution layer, which skips the block. Note that the Conv6-2 directly goes through the first skip connected block to generate D1 without combination with the feature map in the encoder module. The skip connection is beneficial to back-propagation, which can effectively solve the problem of performance degradation of deep convolutional neural networks under extremely deep conditions, as shown in [33]. It speeds up the training of a deeper network and thus makes the learning easier. In addition, the skip connection helps traverse the information in deep neural networks to boost the classification performance.