  A key challenge in continuous sign language recognition (CSLR) is to
efficiently capture long-range spatial interactions over time from the video
input. To address this challenge, we propose TCNet, a hybrid network that
effectively models spatio-temporal information from Trajectories and Correlated
regions. TCNet's trajectory module transforms frames into aligned trajectories
composed of continuous visual tokens. In addition, for a query token,
self-attention is learned along the trajectory. As such, our network can also
focus on fine-grained spatio-temporal patterns, such as finger movements, of a
specific region in motion. TCNet's correlation module uses a novel dynamic
attention mechanism that filters out irrelevant frame regions. Additionally, it
assigns dynamic key-value tokens from correlated regions to each query. Both
innovations significantly reduce the computation cost and memory. We perform
experiments on four large-scale datasets: PHOENIX14, PHOENIX14-T, CSL, and
CSL-Daily, respectively. Our results demonstrate that TCNet consistently
achieves state-of-the-art performance. For example, we improve over the
previous state-of-the-art by 1.5% and 1.0% word error rate on PHOENIX14 and
PHOENIX14-T, respectively.
