  Video action anticipation aims to predict future action categories from
observed frames. Current state-of-the-art approaches mainly resort to recurrent
neural networks to encode history information into hidden states, and predict
future actions from the hidden representations. It is well known that the
recurrent pipeline is inefficient in capturing long-term information which may
limit its performance in predication task. To address this problem, this paper
proposes a simple yet efficient Temporal Transformer with Progressive
Prediction (TTPP) framework, which repurposes a Transformer-style architecture
to aggregate observed features, and then leverages a light-weight network to
progressively predict future features and actions. Specifically, predicted
features along with predicted probabilities are accumulated into the inputs of
subsequent prediction. We evaluate our approach on three action datasets,
namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also
conduct a comprehensive study for several popular aggregation and prediction
strategies. Extensive results show that TTPP not only outperforms the
state-of-the-art methods but also more efficient.
