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With the goal of solving the problem of feature extractors lacking strong supervision training and insufficient time information concerning single‐sequence model learning, a hierarchical sequence memory network with a multi‐level iterative optimisation strategy is proposed for continuous sign language recognition. This method uses the spatial‐temporal fusion convolution network (STFC‐Net) to extract the spatial‐temporal information of RGB and Optical flow video frames to obtain the multi‐modal visual features of a sign language video. Then, in order to enhance the temporal relationships of visual feature maps, the hierarchical memory sequence network is used to capture local utterance features and global context dependencies across time dimensions to obtain sequence features. Finally, the decoder decodes the final sentence sequence. In order to enhance the feature extractor, the authors adopted a multi‐level iterative optimisation strategy to fine‐tune STFC‐Net and the utterance feature extractor. The experimental results on the RWTH‐Phoenix‐Weather multi‐signer 2014 dataset and the Chinese sign language dataset show the effectiveness and superiority of this method. [ABSTRACT FROM AUTHOR]

The interest of science community towards planet Mars has progressively increased in past decades. Rovers are already doing an excellent job in exploring the Martian surface environment and showed that the Red Planet is not suited for life. However, there is a possibility of enabling human exploration by having astronauts take shelter in habitats located within lava tubes, which can provide protection against radiation and dust storms. These tubes are extremely hard to explore with rovers, thus a flying vehicle will be more suitable for such tasks. INGENUITY (Martian helicopter) had already demonstrated that it is possible to fly on Mars by doing small and simple manoeuvres. Based on NASA's design, this article will introduce the development of guidance, navigation, and control operations of a Martian Inspection Drone (MID) that will be capable of finding the lava tube and scan it in real time using a RealSense camera. Linux based system was chosen in the development process, which allow ROS catkin workspace to connect ArduPilot and Gazebo. The navigation is accomplished using a combination of an inertial measurement unit, a camera, and a laser rangefinder. The flight computer runs an optical flow algorithm for control and navigation purpose. Also, a convolutional neural network is used to perform lava tube entrance detection and inspection. • Martian flying drone simulated with ArduPilot, Gazebo and ROS. • Martian environment simulation. • Martian lava tube detection using convolutional neural networks. • Martian lava tube inspection using a realsense camera. [ABSTRACT FROM AUTHOR]

• We would like to submit the manuscript entitled "High-order relational generative adversarial network for video super-resolution", which we wish to be considered as a research article for publication in "Pattern Recognition". Because our contributed research covers the area" Image processing and representation", this manuscript belongs in this journal. No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. • Both the instability of motion compensation and feature aggregation often undermine the SR results of videos. Only the global spatio-temporal dependencies of same scale features are considered as one-order relations, with difficulty in handling the complex motions and context variations. To deal with these issues, we argue that high-order relations should be modelled and applied in the generative strategies to exploit feature alignments and long-range information fusion. In fact, the high-order relations mean that the dependencies of features among pixel positions are mined not only from global feature representations but also through local patch interactions. Moreover, the stronger relations can be further revealed across different scales, by which the aggregation weight matrices with high-order statistics are adaptively determined. The videos are regarded as the complementary fusion of motion and context. Thus, simultaneously capturing the underlying relations of the motion and context is the key to video SR. In this paper, we propose a high-order relational generative adversarial network (HOR-GAN) for accurate video SR, which has strong capacity of alleviating the erroneous motion compensation and merging the useful information among the consecutive frames. • In summary, we highlight the main contributions of this paper as follows: We propose a HOR-GAN framework for highly accurate and realistic video SR. By exploiting high-order relations of feature patches under the construction of the pyramid graphs, the proposed HOR-GAN works well to produce a better feature alignment and fuse more spatio-temporal information. We adopt the dual discriminators to provide spatially coherent feedback to the generator, thus making the generator focus more on fine-grained features. The effectiveness of the proposed method has been justified through the extensive experiments. • We design a motion-aware relation module to accurately align neighboring frames with the reference ones. In this module, a patch-wise matching strategy is first used to build cross-scale correspondences of similar patches. The graph attention layers then adaptively aggregate the local patch features to further decrease the alignment error. We develop a context-aware relation module to make full use of high-order dependencies among all warped feature patches. We introduce the multi-scale graph convolution layers to mine contextual interaction relations for better fusing the spatio-temporal features, in which the position information is encoded to augment the detail recovery. Finally, each pixel is enhanced via a global self-attention. • We deeply appreciate your consideration of our manuscript, and we look forward to receiving comments from the reviewers. If you have any queries, please don't hesitate to contact me. Yours sincerely, Rui Chen Video super-resolution can reconstruct a sequence of high-resolution frames with temporally consistent contents from their corresponding low-resolution sequences. The key challenge for this task is how to effectively utilize both inter-frame temporal relations and intra-frame spatial relations. The existing methods for super-resolving the videos commonly estimate optical flows to align the features of multiple frames based on temporal correlations. However, motion estimation is often error-prone and hence largely hinders the recovery of plausible details. Moreover, high-order contextual dependencies in the feature space are rarely exploited for further enhancing the spatio-temporal information fusion. To this end, we propose a novel generative adversarial network to super-resolve low-resolution videos, which makes full use of patch embeddings and is effective in exploring high-order spatio-temporal relations of the feature patches. Specifically, a motion-aware relation module is designed to handle the alignment between neighboring frames and reference ones. Depending on a patch-matching strategy for adaptive selection of multiple most similar patches, the cross-scale graph is constructed to reliably aggregate these patches using a feature pyramid. Based on the structure of multi-scale graph, a context-aware relation module is developed to capture high-order dependencies among resulting warped patches for better leveraging long-range complementary contexts. To further enhance reconstruction ability, the temporal position information of video sequences is also encoded into this module. Dual discriminators with cycle consistent constraints are adopted to provide more informative feedback to the generator while maintaining the global coherence. Extensive experiments have demonstrated the effectiveness of the proposed method in terms of quantitative and qualitative evaluation metrics. [ABSTRACT FROM AUTHOR]