  Recently, deep autoencoders have gained traction as a powerful method for
implementing goal-oriented semantic communications systems. The idea is to
train a mapping from the source domain directly to channel symbols, and vice
versa. However, prior studies often focused on rate-distortion tradeoff and
transmission delay, at the cost of increasing end-to-end complexity and thus
latency. Moreover, the datasets used are often not reflective of real-world
environments, and the results were not validated against real-world baseline
systems, leading to an unfair comparison. In this paper, we study the problem
of remote camera pose estimation and propose AdaSem, an adaptive semantic
communications approach that optimizes the tradeoff between inference accuracy
and end-to-end latency. We develop an adaptive semantic codec model, which
encodes the source data into a dynamic number of symbols, based on the latent
space distribution and the channel state feedback. We utilize a lightweight
model for both transmitter and receiver to ensure comparable complexity to the
baseline implemented in a real-world system. Extensive experiments on
real-environment data show the effectiveness of our approach. When compared to
a real implementation of a client-server camera relocalization service, AdaSem
outperforms the baseline by reducing the end-to-end delay and estimation error
by over 75% and 63%, respectively.
