a single feature map [@ion][@hypernet]. Then object detection is performed on the fused maps with region-wise subnetworks [@frcnn]. As all objects need to be detected based on the fixed features, the optimization becomes much complex. SSD [@ssd] detects objects on multiple CNN layers. However, the semantic information of former layers is limited, which affects the detection performance of these layers. This is the reason why SSD has much worse performance on smaller objects than bigger objects [@ssd].

![A reverse connection block.[]{data-label="block"}](inv-resnet.pdf){width="0.5\linewidth"}

-0.1 in

-0.2 in

Inspired from the success of residual connection [@resnet] which eases the training of much deeper networks, we propose the reverse connection on traditional CNN architectures. The reverse connection enables former features to have more semantic information. One reverse connection block is shown in Figure \[block\]. Firstly, a deconvolutional layer is applied to the reverse fusion map (annotated as rf-map) $n+1$, and a convolutional layer is grafted on backbone layer $n$ to guarantee the inputs have the same dimension. Then the two corresponding maps are merged by element-wise addition. The reverse fusion map 7 is the convolutional output (with 512 channels by 3$\times$3 kernels) of the backbone layer 7. After this layer has been generated, each 