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

Neural Field Conditioning Strategies for 2D Semantic Segmentation