Iterative Alignment Network for Continuous Sign Language Recognition

Junfu Pu, Wengang Zhou, Houqiang Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4165-4174

In this paper, we propose an alignment network with iterative optimization for weakly supervised continuous sign language recognition. Our framework consists of two modules: a 3D convolutional residual network (3D-ResNet) for feature learning and an encoder-decoder network with connectionist temporal classification (CTC) for sequence modelling. The above two modules are optimized in an alternate way. In the encoder-decoder sequence learning network, two decoders are included, i.e., LSTM decoder and CTC decoder. Both decoders are jointly trained by maximum likelihood criterion with a soft Dynamic Time Warping (soft-DTW) alignment constraint. The warping path, which indicates the possible alignment between input video clips and sign words, is used to fine-tune the 3D-ResNet as training labels with classification loss. After fine-tuning, the improved features are extracted for optimization of encoder-decoder sequence learning network in next iteration. The proposed algorithm is evaluated on two large scale continuous sign language recognition benchmarks, i.e., RWTH-PHOENIX-Weather and CSL. Experimental results demonstrate the effectiveness of our proposed method.