Human activity recognition in videos has been widely studied and recently gained significant advances with deep learning approaches; however, it remains a challenging task. In this paper, we propose novel framework that simultaneously considers both implicit explicit representations of human interactions by fusing information local image where the interaction actively occurred, primitive motion...

Semantic action recognition aims to classify actions based on the associated semantics, which can be applied in video captioning and human-machine interaction. In this paper problem is addressed by jointly learning multiple pose lexicons body parts. Specifically, visual models are learnt, one model with part, characterises likelihood of an observed frame being generated from hidden poses. Moreo...