  We propose two well-motivated ranking-based methods to enhance the
performance of current state-of-the-art human activity recognition systems.
First, as an improvement over the classic power normalization method, we
propose a parameter-free ranking technique called rank normalization (RaN). RaN
normalizes each dimension of the video features to address the sparse and
bursty distribution problems of Fisher Vectors and VLAD. Second, inspired by
curriculum learning, we introduce a training-free re-ranking technique called
multi-class iterative re-ranking (MIR). MIR captures relationships among action
classes by separating easy and typical videos from difficult ones and
re-ranking the prediction scores of classifiers accordingly. We demonstrate
that our methods significantly improve the performance of state-of-the-art
motion features on six real-world datasets.
