Relational Sequential Inference with Reliable Observations (draft)

We present a trainable sequential-inference technique for large relational problems. Our method assumes “reliable observations”, i.e., that each state persists long enough to be reliably inferred from the observations it generates. We learn the resulting “state-inference function” (from observation sequences to underlying hidden states) and develop a heuristic sequential-inference method utilizing the learned function. Empirical results, in relational video interpretation, show significant improvement on both the accuracy and the speed of a variety of recent systems.