Multi-Label Zero-Shot Human Action Recognition via Joint Latent Embedding

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. Due to the fact that annotating video data is laborious and timeconsuming, most of the existing works in human action recognition are limited to a number of small scale benchmark datasets where there are a small number of video clips associated with only a few human actions and a video clip often contains only a single action. In reality, however, there often exist multiple human actions in a video stream. Such a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multilabel learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zeroQian Wang The University of Manchester, UK E-mail: [email protected] Ke Chen (corresponding author) The University of Manchester, UK E-mail: [email protected] shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.