ICLR Presentation on FeatureTracker
Date:
We introduce FeatureTracker, a benchmark designed to test whether models can track objects even as their appearance change. We also design a complex-valued recurrent neural network that relies on phase synchrony to dynamically bind features belonging to the same object. As the object transforms, neurons representing it align their phases, creating a stable temporal signature that supports robust tracking. Across diverse transformations, synchrony-based tracking outperforms appearance-based baselines, showing that temporal coordination provides a powerful inductive bias for feature binding.