  This article articulates the emerging paradigm, sitting at the confluence of
computer vision and wireless communication, to enable beyond-5G/6G
mission-critical applications (autonomous/remote-controlled vehicles,
visuo-haptic VR, and other cyber-physical applications). First, drawing on
recent advances in machine learning and the availability of non-RF data,
vision-aided wireless networks are shown to significantly enhance the
reliability of wireless communication without sacrificing spectral efficiency.
In particular, we demonstrate how computer vision enables {look-ahead}
prediction in a millimeter-wave channel blockage scenario, before the blockage
actually happens. From a computer vision perspective, we highlight how radio
frequency (RF) based sensing and imaging are instrumental in robustifying
computer vision applications against occlusion and failure. This is
corroborated via an RF-based image reconstruction use case, showcasing a
receiver-side image failure correction resulting in reduced retransmission and
latency. Taken together, this article sheds light on the much-needed
convergence of RF and non-RF modalities to enable ultra-reliable communication
and truly intelligent 6G networks.
