Convolutional　neural　networks　(CNNs)　can　be　generally　regarded　as　learning-based　visual　systems　for　computer　vision　tasks.　By　imitating　the　operating　mechanism　of　the　human　visual　system　(HVS),　CNNs　can　even　achieve　better　results　than　human　beings　in　some　visual　tasks.　However,　they　are　primary　when　compared　to　the　HVS　for　the　reason　that　the　HVS　has　the　ability　of　active　vision　to　promptly　analyze　and　adapt　to　specific　tasks.　In　this　article,　a　new　unified　pooling　framework　is　proposed　and　a　series　of　pooling　methods　are　designed　based　on　the　framework　to　implement　active　vision　to　CNNs.　In　addition,　an　active　selection　pooling　(ASP)　is　put　forward　to　reorganize　the　existing　and　newly　proposed　pooling　methods.　The　CNN　models　with　an　ASP　tend　to　have　a　behavior　of　focus　selection　according　to　tasks　during　the　training　process,　which　acts　extremely　similar　to　the　HVS.