  Neural fields are neural networks which map coordinates to a desired signal.
When a neural field should jointly model multiple signals, and not memorize
only one, it needs to be conditioned on a latent code which describes the
signal at hand. Despite being an important aspect, there has been little
research on conditioning strategies for neural fields. In this work, we explore
the use of neural fields as decoders for 2D semantic segmentation. For this
task, we compare three conditioning methods, simple concatenation of the latent
code, Feature Wise Linear Modulation (FiLM), and Cross-Attention, in
conjunction with latent codes which either describe the full image or only a
local region of the image. Our results show a considerable difference in
performance between the examined conditioning strategies. Furthermore, we show
that conditioning via Cross-Attention achieves the best results and is
competitive with a CNN-based decoder for semantic segmentation.
