Reviewerly/exHarmony huggingface dataset:
Adaptive Update Siamese Network Tracking Algorithm with Efficient Channel Attention

Aiming at the problems of low robustness and weak contextual information correlation of existing target tracking models, we propose a target tracking method combining efficient channel attention net (ECA net) and adaptive update net (update net) based on the Siam RPN++ algorithm model. This method takes Siam RPN++ as the baseline, and introduces ECA Net into the feature extraction network, so that the target features can be fully captured while the network scale is comparable, and cross-channel interaction is considered; secondly, before the Region Proposal Network (Region Proposal Network, RPN). The introduction of Update Net can better combine the context information, so that the model can obtain a more realistic and robust target template. In this paper, ablation experiments are performed on the VOT2016 dataset, and compared with Siam RPN++, the expected average overlap rate (EAO) index is improved by 5.6%. We compared experiments with some existing well-known algorithm models on the VOT2018 and VOT2019 datasets, the expected average overlap rates reached 0.424 and 0.291, respectively, with a significant improvement. In addition, the number of tracking loss of the model in this paper also has a significant decrease.
VPCNet: Voxel-Point Cascade for 3D Object Detection

Most current 3D object detectors employ the sophisticated point-voxel fusion mechanisms to obtain high-quality feature representation. However, they rarely consider the hierarchical relationship between points and voxels. We propose a novel detection network, called VPCNet, which fuses voxels and raw points utilizing a cascaded structure to optimize proposals hierarchically. The proposed Hierarchical Head is a two-level detection head, including voxel level and point level. The first level utilizes voxel representation to predict relatively rough boxes. The second level exploits the Corner Point Transformer to model the spatial context relationship between the original points and the previous level boxes to obtain more accurate detection results. A Quadratic Weighted Decoder of Corner Point Transformer is developed with standard transformer decoder to reduces the adverse effect of sparsity on learned features. This design combines computationally efficient voxel-based methods with point-based methods which possess more complete spatial information. Our method outperforms most state-of-the-art 3D object detectors on both KITTI and Waymo Open Datasets.
DC-CNN-Based Quantum State Estimation

In recent years, special attention has been paid to deep learning-based quantum state estimation (QSE). In this paper, we apply deep complex convolutional neural networks (DC-CNNs) to the field of QSE and optimize the network architecture by employing the attention mechanism. The DCCNN-based estimator is trained to fit the mapping from Pauli measurements to each target density matrix, and then each output density matrix is projected onto the nearest subspace that satisfies quantum state constraints. A major advantage of the proposed scheme is that it is capable of extracting sufficient features from incomplete measurements, which contributes to its ability to overcome the absence of partial measurement values. Finally, the superiority of the proposed scheme is highlighted in comparison with the QSE schemes based on the conventional least squares estimation (LSE) and maximum likelihood estimation (MLE).
A Plausibility Enhanced Data Augmentation Method for 3D Human Pose Estimation

In 3D human pose or shape estimation, the 2D-to-3D lifting method is usually applied directly or introduced as an assisted supervision strategy because its training dataset can be augmented easily from the 2D pose data, which thus leads to a more satisfactory result. However, sometimes the data may be generated with implausible poses, and so the training process is polluted accordingly. Related works focus on eliminating the obviously implausible poses but ignore the data being potentially implausible. This brings a risk to the training procedure that has a large number of iterations with data being generated continuously. In this paper, we propose a novel method to enhance the plausibility of the generated poses in detail. We first propose some limitations to distinguish the poses with less plausibility from others, and then further filter the implausible data by calculating the probability a pose being implausible through some factors of human poses. These means weaken the influence of implausible poses on the training effect without sacrificing the richness of training data. Experiments illustrate that the proposed method improves the performance of the trained model well.
Robust Soft Actor Critic Tracking Network

Recently, various object tracking algorithms have been proposed. Reinforcement learning takes a new perspective and transforms the object tracking problem into a Markovian decision process. In this paper, we utilize the reinforcement learning algorithm SAC (Soft Actor Critic Algorithm) to train an intelligent network for object tracking. Compared with other algorithms based on AC framework, SAC utilizes the maximum entropy design to train the agent to focus more on acquiring more channels for beneficial actions, rather than being limited to acquiring a single optimal action. This design improves tracking performance while speeding up the training process. Furthermore, we replace the original feature extraction network with ResNet50 for better feature representation and demonstrate its effectiveness. We compare our algorithm with the visual tracking results of other representative trackers. Meanwhile, the excellent performance of the proposed algorithm is verified by experiments on benchmark datasets OTB100, VOT2016, VOT2018 and LaSOT.
Data-driven Modeling based on Expert Knowledge for Transition Mode of Tilt Trirotor UAV

Focusing on the high cost and complex operation in establishing the transition mode model of tilt trirotor unmanned aerial vehicle (UAV), combined with a fuzzy modeling method based on TSK fuzzy model. Firstly, the flight data of UAV are collected through simulation. Secondly, the membership function of TSK fuzzy model is designed according to the expert experience, and constant variable mass and moment of inertia is added to the data set of the neural network for Supervised learning and rapid convergence. Finally, the predictive control law is designed. Based on simulation platform, the off-line model is simulated under a fixed tilt curve which prove the effectiveness of the method. The experimental results show that this method can solve the flight state variables, such as attitude, altitude and speed, more quickly and accurately, and this method greatly reduces the cost of model identification.
Robust Human Motion Estimation for Human Robot Collaboration

Accurate prediction of human motion trajectory is a key factor to ensure the safety of human-machine collaboration. To improve the safety and effectiveness of human-robot collaboration, the robot must make accurate intention prediction in the early stage of human motion and realize active robot behavior. This paper proposes a robust human upper limb end trajectory prediction algorithm, which can effectively Improve the accuracy of trajectory prediction. The basic framework can be described as follows:1) According to the complex characteristics of human upper limb motion patterns, a robust Gaussian mixture model (RGMM) is proposed to model the motion trajectory. It can consider more comprehensive parameter information in the optimization process, and reduce the impact of traditional algorithms that are sensitive to initial values, easy to fall into local convergence and need a priori clustering value. 2) Gaussian mixture regression (GMR) is used to predict cluster trajectories and obtain statistical values of future trajectories. The experimental results show that the RGMP algorithm proposed in this paper can accurately describe human movement and make predictions, which not only ensures the safety of the human body but also has important significance for improving production efficiency.
3D Hand Pose Estimation Based on Deep Learning and Ensemble Learning

A 3D hand pose estimation method is proposed based on ensemble learning for fixed hand postures. First, a data set with 3D attitude angle tags for fixed hand posture is constructed; Then, a single convolutional neural network is used to fit the hand image and the corresponding 3D attitude angle label; Finally, two different ensemble learning strategies are proposed to improve the performance of a single CNN hand pose estimation model. The experimental results verify the effectiveness and practicability of this hand pose estimation method.
Research on Dual Extreme Learning Machine Based on Residual Structure and its application to process modeling

In order to deal with the high dimensional and high complex process data, a residual structure based dual extreme learning machine model (R-DELM) is proposed. This method proposed in this paper uses dual ELM model: one ELM model is used to establish the process model, which can be viewed as a primary regression model; another one is used to compensate the first ELM model by establish residual-ELM model, which can be viewed as a secondary regression model. In the proposed method, the Kendall correlation coefficient (KCC) is firstly utilized to select the relevant process variables for soft sensor modeling, then the proposed R-DELM model is utilized to compute the key quality output. A real-world production process called Purified Terephthalic Acid (PTA) is applied to investigate the model performance of the R-DELM based soft sensor model. Compared with some other machine learning models, simulation results indicate that R-DELM has the characteristic of high modeling accuracy.
Multi-stage Network with Garments Extraction Module to Improve Image-based Virtual Try-on

In the existing image-based virtual try-on works, the garment fabric is often wrongly retained in the neck, chest and some other positions. To deal with that, a multi-stage network with garments extraction module is proposed to enhance the quality of the generated images at the aforementioned area. The proposed network consists of four modules: the garment extraction module, garment warping module, semantic layout generation module and content fusion module. Our main work are the import of the garment extraction module and the improvement of the semantic layout generation module. The proposed method is evaluated qualitatively on the virtual try-on dataset VITON and a clothing dataset collected from the Internet which is mainly consists of hoodies and low necklines. Compared with the current virtual try-on networks CP-VTON and ACGPN, the proposed approach obtains more natural images especially in some specific areas such as neck and chest. Despite the addition of extra modules, our work cost only 0.02ms extra compared to ACGPN.
Dynamic Trajectory Planning and Tracking Algorithm of Lunar Rover with Updating Map Information

At present, the remote sensing map still cannot perfectly describe the detailed characteristics of the moonscape, which poses a challenge to the trajectory planning of the lunar rover in the application of moonscape exploration and survey missions. This paper proposes a dynamic trajectory planning and tracking algorithm based on real-time updating of moonscape map information. Firstly, the large-scale telemetry map is rasterized. The Chebyshev distance is used to construct the cost function of grid node connection, and the node connection trajectory with the lowest cost is selected as the initial trajectory. The pure tracking algorithm is used to track the trajectory, and the topographical information of the map is updated in the process. When obstacle nodes (namely the terrain that exceeds the rover’s own motion ability) are perceived in the initial trajectory, the cost of obstacle nodes and surrounding nodes is corrected, so as to update and optimize the trajectory. The effectiveness of the dynamic trajectory planning and tracking algorithm proposed in this paper is verified by simulation. Compared with other methods, this algorithm can be adapted for the application scenarios with incomplete map information, as well as efficiently update the optimal trajectory and maintain high precision trajectory tracking. So that the safety of the lunar rover moving on the moonscape can be guaranteed.
Obstacle-Avoidance Path Planning of Unmanned Vehicles based on Polynomial Optimization

In order to solve the obstacle avoidance path planning problem of automatic driving vehicles on unstructured roads, an A* algorithm is used to search for the collision-free path. The path waypoints are compressed by the RDP (Ramer-Douglas-Peucker) algorithm [5]. A polynomial is used to fit the path between two adjacent waypoints to ensure the continuity of vehicle speed, acceleration, jerk (derivative of acceleration), and snap (derivative of jerk), so that the unmanned vehicle drives smoothly. Finally, five S-curves are used to complete the speed-time planning of the path, and the colliding parts on the path are filled by interpolation. Simulation results show that this method can effectively and quickly complete global path planning. It can also be applied to real-time planning scenarios.
Deep Image Stitching with Pixel Similarity Correlation

Image stitching is a task of synthesising images from different views without blur, seams and ghost effects. However, it is hard for existing methods to get all these visual problems solved. In this paper, we propose an image pre-reconstruction network to improve the similarity of images from different views in pixel level, which can eliminate ghost effects and correct some deviations in the image alignment step. Given the overlapping areas by warping two original images, we extract their features and calculate the distance map. According to the distance map, we classify the receptive field and define two kinds of loss functions to achieve different convergence effects. In addition, to obtain a seamless stitched image, we define a gradient loss to smooth the edge of overlapping area by narrowing the gradient between adjacent pixels. At last, we apply this gradient loss to all the image area to get a smooth effect for the whole image. Experiment shows that our method performs better than some existing methods on visual effect in the final composite.
A Real-time Adaptive Filtering Algorithm for Reentry Maneuvering

In order to solve the problem of the real-time reconstruction of the reentry trajectory with lift-to-drag controlling law during the spacecraft’s reentry flight, an adaptive UKF filtering algorithm was proposed by using external measurements and considering the actual engineering situations. The filter’s state model and observation model were given, and some adaptive processing methods were used, including the maneuver detection and model switching method, and the acceleration compensation method, which were used to deal with the real-time tracking of the maneuvering process of the return flight. The simulation results show that the proposed calculation method is feasible, and the proposed adaptive processing algorithm applying the acceleration compensation can do the robust estimation processing of the reentry trajectory. The research results have certain reference value for the real-time estimation of maneuvering reentry spacecraft.
Curved Text Detection Based on Graph Convolution Context Enhancement

Text detection in industrial scenarios faces a major challenge: how to design smaller models while maintaining high detection accuracy. We propose a curved text detection model based on graph convolution context enhancement. By using the lightweight backbone MobileNetV2 to reduce the parameter count, and using the graph convolution context module to enhance context modeling ability. The target scene of this paper is the detection of painting characters on steel coils in the production process. Experiments on the Total-Text and the steel coil painting characters dataset show that the proposed method can maintain the optimal detection results with fewer parameters. Graph convolution context module makes the F-measure of this model on the Total-Text test set optimal among the methods based on lightweight networks such as ResNet18 and smaller networks. On the steel coil painting characters dataset, the F-measure of our method is also ahead of PAN and PSENet, and achieves real-time performance.
Research on UUV path planning method based on bidirectional A* algorithm

The efficient and reliable route planning of unmanned underwater vehicle (UUV) is very important for its safe avoidance of obstacles and successful completion of tasks. Aiming at the problem that the standard A* algorithm is easy to fall into local minima, and the planning efficiency is low, which leads to low real-time performance, the bidirectional A* algorithm, which searches the path from the first point and the end point at the same time, is used for UUV path planning. Besides, aiming at the problem that the fidelity of the two- dimensional simulation environment is not high and it is difficult to depict the obstacle information in three-dimensional, the three-dimensional route planning scene is further constructed to test the performance of algorithms. The simulation results show that compared with the standard A* algorithm, the bidirectional A* algorithm can better take into account the efficiency and quality, and the planned path is safe and reasonable, meeting the obstacle avoidance requirements of UUV in three- dimensional space.
CMFST : Class-based Multi-scale Fusion Self-training for Adapting Semantic Segmentation

Learning domain-invariant representations has been adopted by mainstream approaches of unsupervised domain adaptation (UDA) to alleviate the domain gap between source data with rich label and unlabeled target data. Self-training is a simple and popular method in UDA by training networks with target pseudo-labels. However, the pseudo-labels’ quality is limited by the single scale output of unadapted network, which has an important impact on training process. To address this issue, we propose a class-based multi-scale fusion (CMF) method that allows the unadapted network to achieve better prediction results by confirming class-scale relation and fusing specific classes prediction from appropriate scales. Our approach doesn’t rely on complex components, therefore is simple, efficient, and easy to transfer. We achieve competitive performance (measured by the Intersection-over-Union, IoU) in the GTA5 to Cityscapes and the SYNTHIA to Cityscapes scenarios which demonstrates the effectiveness of our method.
Unsupervised Optimal Anomaly Detection Model Selection in Power Data

Power data is complex and diverse. Different data types correspond to different power anomaly monitoring models. How to use a variety of feature combinations to automatically screen the optimal power anomaly detection model in the scenario of unsupervised power data anomaly detection is an urgent problem to be solved. First, extract the complex power data features into seven types of eigenvalues. Then, using the selection algorithm for unsupervised anomaly detection models based on the METAOD method, the optimal selection results of anomaly detection models under various power data sets are used to generate a selection database. Finally, divide the seven types of features into different combinations and use the reward principle and the corresponding abnormal detection results to combine and screen the optimal feature combination and the optimal power abnormality monitoring model for the existing data.
Generalized Zero-shot Learning Fault Diagnosis Framework Based on Anomaly Detection and Contractive Stacked Autoencoder

Recently, data-driven fault diagnosis approaches have shown competitive results. However, some fault samples never occur or are difficult to be obtained, which are called zero-sample. Since a training data set containing zero-sample fault cannot be created, traditional deep learning methods can not realize fault diagnosis of zero-sample. To settle this issue, a fault diagnosis framework based on anomaly detection and contractive stacked autoencoder (CSA) is proposed to achieve zero-shot learning, which consists of a zero-sample fault discriminator, a feature extractor and a fault classifier. After training CSA by using the samples of seen fault classes, the extracted features and the error between the reconstruction and the input of CSA are utilized to construct a novel zero-sample fault discriminator. When the dataset consists of seen classes and unseen classes, the designed discriminator can distinguish whether the test samples belong to the unseen classes. If the test samples belong to the unseen classes, it can be further identified by the designed discriminator; If the test samples belong to the seen classes, it is classified by supervised learning. By using the Case Western Reserve University (CWRU) dataset, the effect of our proposed framework is verified.
A Lightweight 3D Object Detection Model Based on Stereo Methods

In recent years, with the development of intelligent video surveillance, robot navigating and automatic driving, 3D object detection is becoming an important field of computer vision. Most methods are based on deep neural networks and use information obtained with sensors such as LiDAR and binocular or monocular cameras. Methods based on binocular images are relatively cheap and less independent on computation device compared to point cloud data from LiDAR, and provide depth information that monocular image does not contain. In this sense, a lightweight detection approach based on stereo images has great practical significance in some scenarios with certain requirements.Based on discussion above, we study on lightweight improvements based on the latest YOLOStereo3D model, and weigh the performance of different lightweight backbones on this task. At the same time, we propose two improvements on the depth feature extraction structure, aiming to reduce the accuracy loss brought with lightweight backbone. Finally, we obtain a relatively lightweight detection model that can maintain sufficient accuracy for certain scenarios.
A Hierarchal BERT Structure for Native Speaker Writing Detection

Native speaker detection has always focused on speech data. However, the writing style of native speakers is also different from that of non-native speakers. Therefore, for the first time, we performed native speaker detection on text data so that non-native speakers can better learn the writing style of native speakers. Native speaker writing detection is relatively difficult due to the long sequences and complex semantics in the writings. Therefore, we use BERT-based methods. However, BERT suffers from the exponentially increasing computational complexity because of the self-attention mechanism, which limits the length of text input. Consequently, in this paper, we present a hierarchical BERT model to solve this problem. Our model first cuts the long text into segments and obtains segment representation vectors from BERT. Then, we extract the temporal and interactional information between segments to form a text-level representation vector for writing detection. We conducted experiments on a self-made native speaker writing detection dataset. The results demonstrate that our model can accurately recognize native speakers' writing. In addition, we have successfully used it in various long text classification tasks and achieved improvement over the baseline models. We also show the importance of both temporal and interaction information for text-level representation.