  Continuous sign language recognition (cSLR) is a public significant task that
transcribes a sign language video into an ordered gloss sequence. It is
important to capture the fine-grained gloss-level details, since there is no
explicit alignment between sign video frames and the corresponding glosses.
Among the past works, one promising way is to adopt a one-dimensional
convolutional network (1D-CNN) to temporally fuse the sequential frames.
However, CNNs are agnostic to similarity or dissimilarity, and thus are unable
to capture local consistent semantics within temporally neighboring frames. To
address the issue, we propose to adaptively fuse local features via temporal
similarity for this task. Specifically, we devise a Multi-scale Local-Temporal
Similarity Fusion Network (mLTSF-Net) as follows: 1) In terms of a specific
video frame, we firstly select its similar neighbours with multi-scale
receptive regions to accommodate different lengths of glosses. 2) To ensure
temporal consistency, we then use position-aware convolution to temporally
convolve each scale of selected frames. 3) To obtain a local-temporally
enhanced frame-wise representation, we finally fuse the results of different
scales using a content-dependent aggregator. We train our model in an
end-to-end fashion, and the experimental results on RWTH-PHOENIX-Weather 2014
datasets (RWTH) demonstrate that our model achieves competitive performance
compared with several state-of-the-art models.
