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There are many applications in which the robot is required to follow a specific visual cue. One newly emerging method for autonomous navigation along a consistent visual cue is Visual Teach and Generalise (VTAG). It is a unified approach to extract suitable features for path following in repetitive and structured environments. However, a relatively large number of data samples is required for the time-consuming training of a detector tied to a specific visual cue. We introduce an improved version of VTAG that does not need any neural network training and significantly reduces the requirements for the number of data samples. The core idea is to use an average representation of images collected during short supervised traversals, capturing characteristics of repetitive paths. The average representation is created in a latent space crafted by contrastive learning with the linear matching scheme and can be computed in real time, even on a CPU. 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