A Zero-Shot Architecture for Action Recognition in Still Images
Marjaneh Safaei, Hassan Foroosh · 2018
Motion is a missing information in an image, however, it is a valuable cue for action recognition. Therefore, not only actions depend on the spatial-salient pixels, but also the temporal patterns of those pixels are evidently crucial. In this paper, we propose a novel unsupervised zero-shot approach, employing both spatial and temporal patterns, to perform action recognition in still images through Tensor Decomposition. In the proposed model, (1) we devise a novel strategy to form tensors from individual images in a way that each tensor encodes useful spatial-temporal information regarding the action being performed in images. Tensor decomposition is then used to estimate the overall signature of the action, while action is encoded in the spatial-temporal descriptions of images. (2) We show that appearance and motion are complementary sources of information. Comprehensive experiments on four benchmarks: Stanford-40, Willow, WIDER and the newly introduced UCFSI -101 still images dataset clearly demonstrate that our method outperforms state-of-the-art approaches.