  Hand and face play an important role in expressing sign language. Their
features are usually especially leveraged to improve system performance.
However, to effectively extract visual representations and capture trajectories
for hands and face, previous methods always come at high computations with
increased training complexity. They usually employ extra heavy pose-estimation
networks to locate human body keypoints or rely on additional pre-extracted
heatmaps for supervision. To relieve this problem, we propose a
self-emphasizing network (SEN) to emphasize informative spatial regions in a
self-motivated way, with few extra computations and without additional
expensive supervision. Specifically, SEN first employs a lightweight subnetwork
to incorporate local spatial-temporal features to identify informative regions,
and then dynamically augment original features via attention maps. It's also
observed that not all frames contribute equally to recognition. We present a
temporal self-emphasizing module to adaptively emphasize those discriminative
frames and suppress redundant ones. A comprehensive comparison with previous
methods equipped with hand and face features demonstrates the superiority of
our method, even though they always require huge computations and rely on
expensive extra supervision. Remarkably, with few extra computations, SEN
achieves new state-of-the-art accuracy on four large-scale datasets, PHOENIX14,
PHOENIX14-T, CSL-Daily, and CSL. Visualizations verify the effects of SEN on
emphasizing informative spatial and temporal features. Code is available at
https://github.com/hulianyuyy/SEN_CSLR
