Continuous Sign Language Recognition With Correlation Network

Human body trajectories are a salient cue to identify actions in video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition(CSLR) usually process frames independently to capture frame-wise features, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly leverage body trajectories across frames to identify signs. In specific, an identification module is first presented to emphasize informative regions in each frame that are beneficial in expressing a sign. A correlation module is then proposed to dynamically compute correlation maps between current frame and adjacent neighbors to capture cross-frame trajectories. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison between CorrNet and previous spatial-temporal reasoning methods verifies its effectiveness. Visualizations are given to demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.