Abstract
Recently, zero-shot action recognition (ZSAR) has emerged with the explosive growth of action categories.
In this paper, we explore ZSAR from a novel perspective
by adopting the Error-Correcting Output Codes (dubbed
ZSECOC). Our ZSECOC equips the conventional ECOC
with the additional capability of ZSAR, by addressing the
domain shift problem. In particular, we learn discriminative ZSECOC for seen categories from both categorylevel semantics and intrinsic data structures. This procedure deals with domain shift implicitly by transferring the
well-established correlations among seen categories to unseen ones. Moreover, a simple semantic transfer strategy
is developed for explicitly transforming the learned embeddings of seen categories to better fit the underlying structure
of unseen categories. As a consequence, our ZSECOC inherits the promising characteristics from ECOC as well as
overcomes domain shift, making it more discriminative for
ZSAR. We systematically evaluate ZSECOC on three realistic action benchmarks, i.e. Olympic Sports, HMDB51 and
UCF101. The experimental results clearly show the superiority of ZSECOC over the state-of-the-art methods