Millimeter-wave　(mmWave)　communications　with　abundant　spectrum　resources　have　become　an　enabling　technology　for　high　throughput,　ultra-reliable,　and　low　latency　communications　(URLLC).　Since　the　mmWave　signal　is　sensitive　to　blockage,　accurate　base　station　(BS)　selection　is　the　premise　of　achieving　the　URLLC.　In　this　paper,　we　propose　a　multi-view　images　assisted　proactive　BS　selection　scheme　that　can　predict　the　optimal　BS　for　the　user　in　the　next　frame.　The　proposed　scheme　utilizes　vision　sensing　and　thus　does　not　require　the　entire　pilot　resources,　such　that　the　latency　caused　by　seeding　and　receiving　pilots　reduces.　In　addition,　we　design　a　multi-task　learning　strategy　and　a　prior　knowledge　based　fine　tuning　method　to　ensure　the　accuracy　and　reliability　of　BS　selection.　Simulation　results　in　an　outdoor　environment　demonstrate　the　superior　performance　of　the　proposed　scheme　in　terms　of　both　the　accuracy　and　the　robustness.