[@ZhouAl:cvpr16] are applied to visualize the responses at different convolutional layers for generating pedestrian proposals. In general, lower representation layers may have a strong activation of convolutional neurons for far-scale pedestrians. Similar phenomenon for near-scale pedestrian instances usually emerge at higher layers. Moreover, higher layer neurons tend to encode more global and semantic information of objects that could be robust against significant appearance variations, while their spatial resolutions tend to be overly coarse, thus unable to accurately locate far-scale pedestrian instances. On the other hand, outputs of lower convolutional layers provide more precise localization, while being less invariant to objects’ appearance changes. It can be seen from Fig. \[fig\_3\] that the first two layers namely ResNet-50-C1 and ResNet-50-C2 could be saturated by the amount of false alarms from backgrounds, while ResNet-50-C3 seems a satisfactory starting layer for effective multi-layer representation. For near-scale objects such as the one at the center location of Fig. \[fig\_3\], their feature responses appear across multiple layers, yet the last two layers and especially the very last layer here seems to retain best responses. The feature responses from the rest (i.e. the first two) layers are still noticeable but with more unnecessary local or even background 