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

Raw videos have been proven to own considerable feature redundancy where in many cases only a portion of frames can already meet the requirements for accurate recognition. In this paper, we are interested in whether such redundancy can be effectively leveraged to facilitate efficient inference in continuous sign language recognition (CSLR). We propose a novel adaptive model (AdaBrowse) to dynamically select a most informative subsequence from input video sequences by modelling this problem as a sequential decision task. In specific, we first utilize a lightweight network to quickly scan input videos to extract coarse features. Then these features are fed into a policy network to intelligently select a subsequence to process. The corresponding subsequence is finally inferred by a normal CSLR model for sentence prediction. As only a portion of frames are processed in this procedure, the total computations can be considerably saved. Besides temporal redundancy, we are also interested in whether the inherent spatial redundancy can be seamlessly integrated together to achieve further efficiency, i.e., dynamically selecting a lowest input resolution for each sample, whose model is referred to as AdaBrowse+. Extensive experimental results on four large-scale CSLR datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily and CSL, demonstrate the effectiveness of AdaBrowse and AdaBrowse+ by achieving comparable accuracy with state-of-the-art methods with 1.44X throughput and 2.12X fewer FLOPs. Comparisons with other commonly-used 2D CNNs and adaptive efficient methods verify the effectiveness of AdaBrowse. Code is available at https://github.com/hulianyuyy/AdaBrowse.

With the development of society and the economy, low-carbon and low-energy means of exploiting marine resources are receiving increasing attention. Autonomous path planning is a fundamental capability for IoT autonomous underwater vehicle (AUV) to carry out ocean exploration tasks. Currently, the main issue lies in the numerous disturbances and uncertainties present in the marine environment during practical applications, which can significantly impact path planning, leading to high-energy consumption and carbon emissions. To address this challenge, this article presents a sustainable reinforcement learning algorithm for handling time-varying current disturbances to achieve low-carbon AUV path planning, which is delineated into three steps. First, a 3-D time-varying current environment is established as the environmental framework for reinforcement learning, and the dynamic model of the AUV is formulated. Second, to enhance training efficiency and reduce AUV’s energy consumption, this article puts forth the ocean current disturbance rejection PPO (OCDRP) algorithm, which incorporates tidal current information to enhance the AUV’s resilience to time-varying currents. Lastly, expectile regression methods are introduced to facilitate the algorithm’s convergence. Experimental results confirm the efficacy of the proposed algorithm and its adaptability to time-varying currents, making it an efficient, adaptable, and low-carbon sustainable path planning approach.