On Diverse Asynchronous Activity Anticipation
We investigate the joint anticipation of long-term activity labels and their corresponding times with the aim of improving both the naturalness and diversity of predictions. We address these matters using Conditional Adversarial Generative Networks for Discrete Sequences. Central to our approach is a reexamination of the unavoidable sample quality vs. diversity tradeoff of the recently emerged Gumbel-Softmax relaxation based GAN on discrete data. In particular, we ameliorate this trade-off with a simple but effective sample distance regularizer. Moreover, we provide a unified approach to inference of activity labels and their times so that a single integrated optimization succeeds for both. With this novel approach in hand, we demonstrate the effectiveness of the resulting discrete sequential GAN on multimodal activity anticipation. We evaluate the approach on three standard datasets and show that it outperforms previous approaches in terms of both accuracy and diversity, thereby yielding a new state-of-the-art in activity anticipation."