Efficient motion capture data recovery via relationship-aggregated graph network and temporal pattern reasoning.

Human motion capture (mocap) data is of crucial importance to the realistic character animation, and the missing optical marker problem caused by marker falling off or occlusions often limit its performance in real-world applications. Although great progress has been made in mocap data recovery, it is still a challenging task primarily due to the articulated complexity and long-term dependencies in movements. To tackle these concerns, this paper proposes an efficient mocap data recovery approach by using Relationship-aggregated Graph Network and Temporal Pattern Reasoning (RGN-TPR). The RGN is comprised of two tailored graph encoders, local graph encoder (LGE) and global graph encoder (GGE). By dividing the human skeletal structure into several parts, LGE encodes the high-level semantic node features and their semantic relationships in each local part, while the GGE aggregates the structural relationships between different parts for whole skeletal data representation. Further, TPR utilizes self-attention mechanism to exploit the intra-frame interactions, and employs temporal transformer to capture long-term dependencies, whereby the discriminative spatio-temporal features can be reasonably obtained for efficient motion recovery. Extensive experiments tested on public datasets qualitatively and quantitatively verify the superiorities of the proposed learning framework for mocap data recovery, and show its improved performance with the state-of-the-arts.

Relation-Aggregated Cross-Graph Correlation Learning for Fine-Grained Image-Text Retrieval.

Fine-grained image-text retrieval has been a hot research topic to bridge the vision and languages, and its main challenge is how to learn the semantic correspondence across different modalities. The existing methods mainly focus on learning the global semantic correspondence or intramodal relation correspondence in separate data representations, but which rarely consider the intermodal relation that interactively provide complementary hints for fine-grained semantic correlation learning. To address this issue, we propose a relation-aggregated cross-graph (RACG) model to explicitly learn the fine-grained semantic correspondence by aggregating both intramodal and intermodal relations, which can be well utilized to guide the feature correspondence learning process. More specifically, we first build semantic-embedded graph to explore both fine-grained objects and their relations of different media types, which aim not only to characterize the object appearance in each modality, but also to capture the intrinsic relation information to differentiate intramodal discrepancies. Then, a cross-graph relation encoder is newly designed to explore the intermodal relation across different modalities, which can mutually boost the cross-modal correlations to learn more precise intermodal dependencies. Besides, the feature reconstruction module and multihead similarity alignment are efficiently leveraged to optimize the node-level semantic correspondence, whereby the relation-aggregated cross-modal embeddings between image and text are discriminatively obtained to benefit various image-text retrieval tasks with high retrieval performance. Extensive experiments evaluated on benchmark datasets quantitatively and qualitatively verify the advantages of the proposed framework for fine-grained image-text retrieval and show its competitive performance with the state of the arts.

A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification.

With the continuous development of the convolutional neural network (CNN) concept and other deep learning technologies, target recognition in Synthetic Aperture Radar (SAR) images has entered a new stage. At present, shallow CNNs with simple structure are mostly applied in SAR image target recognition, even though their feature extraction ability is limited to a large extent. What's more, research on improving SAR image target recognition efficiency and imbalanced data processing is relatively scarce. Thus, a lightweight CNN model for target recognition in SAR image is designed in this paper. First, based on visual attention mechanism, the channel attention by-pass and spatial attention by-pass are introduced to the network to enhance the feature extraction ability. Then, the depthwise separable convolution is used to replace the standard convolution to reduce the computation cost and heighten the recognition efficiency. Finally, a new weighted distance measure loss function is introduced to weaken the adverse effect of data imbalance on the recognition accuracy of minority class. A series of recognition experiments based on two open data sets of MSTAR and OpenSARShip are implemented. Experimental results show that compared with four advanced networks recently proposed, our network can greatly diminish the model size and iteration time while guaranteeing the recognition accuracy, and it can effectively alleviate the adverse effects of data imbalance on recognition results.

Development and application of a water pollution emergency response system for the Three Gorges Reservoir in the Yangtze River, China.

There are many watercraft and production accidents in the Three Gorges Reservoir Area (TGRA) of the Yangtze River in China every year. Accidents threaten the water quality of the 1085 km2 surface area of the TGRA and millions of local people if oil and chemical leakage were to occur. A water pollution management system for emergency response (WPMS_ER) was therefore designed for the management of pollution in this area. An integrated geographic information system (GIS)-based water pollution management information system for the TGRA, called WPMS_ER_TGRA, was developed in this study. ArcGIS engine was used as the system development platform, and Visual Basic as the programming language. The models for hydraulic and water quality simulation and the generation of body-fitted coordinates were developed and programmed as a dynamically linked library file using Visual Basic, and they can be launched by other computer programs. Subsequently, the GIS-based information system was applied to the emergency water pollution management of a shipwreck releasing 10 tons of phenol into the Yangtze River during two hours. The results showed that WPMS_ER_TGRA can assist with emergency water pollution management and simulate the transfer and diffusion of accidental pollutants in the river. Furthermore, it can quickly identify the affected area and how it will change over time within a few minutes of an accident occurring.