Reviewerly/exHarmony huggingface dataset:
A High-precision Quantization Method of Neural Networks for Concat Operator

The deployment of neural networks on the embedded devices is an important development trend in the future. Due to the limited resources on the embedded devices, the acceleration of neural networks is urgent and of great importance. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is one of the most promising solutions for reducing the computing and storage cost of neural networks on the embedded devices. Concat is a common operator of neural networks. Since all the branches that are being concatenated should share the same quantization parameters, the quantization of the Concat operator results in a large loss of accuracy. It is the bottleneck problem that hinders the application of neural networks on embedded devices. In this paper, a high-precision quantization method for the Concat operator is proposed. The proposed method can replace the quantization of the Concat operator with the quantization of the Eltwise operator, which can effectively reduce the quantization accuracy loss. The results show that the accuracy of the proposed method can be improved up to 2.19% for classification, segmentation and small target detection applications.
Text2Time: Transformer-based Article Time Period Prediction

The prediction of the publication period of textual documents, such as news articles, represents a significant and relatively understudied problem within the realm of natural language processing. Determining the year in which a news article was published holds relevance in various domains, including historical research, sentiment analysis, and media monitoring. In this research, our focus is on investigating the prediction of publication periods specifically for news articles, leveraging their textual content. To tackle this challenge, we curated an extensive labeled dataset consisting of over 350,000 news articles published by The New York Times over a span of six decades. This dataset forms the foundation of our investigation. Our approach involves utilizing a pretrained BERT model that has been fine-tuned for the task of text classification, specifically tailored for time period prediction. The performance of our model surpasses our initial expectations, demonstrating impressive results in accurately classifying news articles into their respective publication decades. Through rigorous evaluation, our model outperforms the baseline model for this relatively unexplored task of predicting time periods based on textual content. This research sheds light on the potential for effectively predicting the publication periods of news articles and presents promising outcomes achieved by leveraging a pretrained BERT model fine-tuned for time period classification. The results obtained contribute to the advancement of this underexplored task, demonstrating the viability and accuracy of time prediction from textual data.
An Attention-based Self-Supervised Approach to Monocular Depth Estimation from UAV captured Video Sequences

UAV-based inspections represent a promising solution to contactless civil infrastructure condition assessment, especially bridges. In this context, depth estimation is crucial as it helps predict the distance to objects, plan flight trajectories, and avoid obstacles. Recently, learning-based methods made significant progress in monocular depth estimation. However, supervised approaches require a large amount of annotated depth data which remains challenging to collect. As an alternative, researchers have been investigating self-supervised learning, where the depth estimation network is trained by projecting a view to nearby views and minimizing the re-projection photometric loss. However, current methods lack accuracy and robustness since they do not exploit structural information, leading to inaccurate spatial representations and surface discontinuities. In addition, monocular depth estimation suffers from scale ambiguity, and external or prior information is needed for scale recovery. This work sought to overcome these issues by adapting the standard joint network of estimating depth and pose based on a self-learned depth prediction network from monocular videos. The proposed method introduces a soft attention mechanism and a recurrent block. The attention mechanism guides the neural network’s attention to the global structures and local attributes and recovers the spatial relationship of visual features, whereas the recurrent block leverages sequence frames to capture temporal information in addition to spatial information. Our experiments and ablation study on AeroBridge dataset demonstrate that these modifications efficiently capture spatio-temporal information and generate competitive qualitative and quantitative results compared to state-of-the-art methods confirming the practical applicability of our design in inspection tasks.
Complex Small Target Image Recognition Algorithm Based on Data Enhancement in YOLOv7

The recognition of small and complex target image is a key problem in the scientific research field, which has very important scientific value. Although FASTER-RCNN and YOLOV5 can recognize complex targets, the recognition accuracy is not high due to the difference of network structure and the application of activation function. For the samples of six kinds of insects, the scene is complex, most of the targets overlap each other, and the recognition is difficult. Firstly, this article preprocesses the image using image data augmentation technology and uses YOLOv7 for image recognition. The RELU activation function is used, with an accuracy of 0.903, Recall reaches 0.864, and mAP reaches 0.929. After comparing this algorithm with FASTER-RNN, the results show that YOLOV7 has better recognition performance and performance.
Swin Transformer-based Cross-view Attention Network for Stereo Image Super-Resolution

Stereo image pairs offer complementary information that can improve the performance of image super-resolution reconstruction significantly. However, integrating longrange dependencies between stereo image pairs into super-resolution is challenging. This paper proposes a Swin Transformer-based cross-view attention network for stereo image super-resolution (TCASSRnet) to address this issue. Firstly, TCASSRnet extracts stereo image pair features using a Transformer with a shallow residual block (SRB). Next, a cross-view attention model (CAM) is introduced to integrate complementary features of stereo image pairs, resulting in high-frequency and low-frequency attention maps. Finally, deep residual blocks (DRBs) are employed to restore image details layer by layer and reconstruct super-resolution images. Extensive experimental results demonstrate the effectiveness of TCASSRnet. Specifically, TCASSRnet outperforms the baseline network (PASSRnet) by 0.55 dB in terms of PSNR on the Middlebury dataset for 4× SR, while the parameter size of TCASSRnet is reduced by 50% compared to the recent SSRDE-Fnet.
Using Improved YOLOv5 for Underwater Target Detection

In recent years, as the exploitation and utilization of marine resources have increased, underwater target detection has become increasingly important. Improving related technologies holds significance for resource utilization and extraction. Although existing target detection algorithms have achieved good results on land, they face challenges such as low accuracy and slow speed in underwater environments. To address this issue, this paper proposes an improved underwater target detection algorithm called YOLOv5-GCB. Firstly, the algorithm adopts the GhostNet network to reduce computational complexity. Secondly, an attention mechanism is introduced after the last C3 module in the Backbone, allowing the model to focus on extracting target features and highlighting key characteristics. Furthermore, a weighted bidirectional feature pyramid structure is employed in the Neck section, utilizing a bidirectional weighting approach that integrates position information from lower-level feature maps with semantic information from higher-level feature maps. Finally, we applied the improved model to our self-collected underwater image dataset for experimentation. The experimental results show that the YOLOv5-GCB model achieves an average precision (mAP) of 82.9% and a recall rate of 76.5% on the test dataset. Compared to the baseline model (YOLOv5), the performance of the improved model has improved by 5.5% and 5.8% respectively.
Wireless Emergency Communication Method based on Electromagnetic Spectrum Environment Sensing

The occurrence of major disasters will have a great impact on local social stability and the safety of people’s lives and property. When a disaster occurs, the emergency communication network should be established quickly, quickly contact with the disaster area, fully understand the current situation of the disaster area, and in the first time to deploy and arrange relief work in the disaster area, to minimize the loss of disaster.With the development of wireless communication technology, various wireless devices emerge one after another, and spectrum resources become scarce resources, so it is necessary to improve the utilization rate of spectrum resources.In this paper, the spectrum detection method of I/Q data is applied to emergency communication, and a wireless communication system is proposed which can sense the electromagnetic spectrum environment of the target region in real time, access the electromagnetic spectrum resources dynamically and resist electromagnetic spectrum interference. The I/Q data spectrum detection method is more accurate than the commonly used energy detection method to distinguish weak signals from noise. The system can independently establish and maintain highly reliable wireless communication links in complex geographical environments.
Research on Underwater Image Restoration Algorithm Based on Generative Adversarial Network

Underwater optical imaging is prone to image distortion, blurring and other problems arising from thermal disturbance. To tackle down the above-mentioned problems, underwater images affected by thermal disturbance are analyzed in this study from two perspectives (i.e., image gray histogram distribution and similarity assessment). Subsequently, a deep learning-based underwater thermal disturbance image restoration algorithm is developed based on generative adversarial networks. The U-Net structure is employed in the generator network, and MultiRes blocks are adopted in the contracting and expanding paths for extracting image feature information on different scales maximally. Moreover, a Bottleneck Attention Module (BAM) is introduced in the contracting path to place a full focus on the distorted image information. High-precision image restoration is achieved using the Wasserstein adversarial loss function in the adversarial process between the generator and discriminator networks. To validate the algorithm, an underwater thermal imaging experimental platform is independently built, and a dataset is established. As indicated by the experimental results, the proposed algorithm is capable of effectively restoring images while preserving key edge features, such that a Structural Similarity (SSIM) index of 0.7749 and a Peak Signal to Noise Ratio (PSNR) index of 21.28 are generated. This study can lay a solid foundation for further processing in underwater image restoration.
HCVNet+: A Lightweight and Accurate Network for Binocular Stereo Matching

Binocular stereo matching, the foundation of many visual and robotic applications, typically estimates disparity and depth from the cost volume constructed by the left and right feature maps. Although most learning-based models can achieve superb effects, it is still difficult to satisfy the demand for a better trade-off between accuracy and efficiency. Thus, our main aim is to enable the backbone model become lightweight and efficient without sacrificing accuracy. Firstly, to achieve saving in computation, we present the 3D effective channel attention module(3DECAM) to make the target cost volume fetch more helpful information from the source cost volume. Then, in order not to greatly affect the accuracy, we design the residual-block-style attention hourglass module(RAHM) to appropriately aggregate and utilize the input cost volume. At last, combining these ideas, we construct a lightweight and efficient end-to-end stereo matching network called HCVNet+. Under the same running condition, HCVNet+ can achieve 0.669 EPE with 220ms avg run time and 135G MACs, which is faster than HCVNet(260ms avg run time), also descending HCVNet’s MACs(268G) by almost 50%.
Target Detection Algorithm for Foggy Scenes Based on Twin Network

In real scenes, the quality and contrast of images are reduced due to the uneven state of haze caused by factors such as humidity, dust, and aerosols in the air. In this scenario, it is difficult for a general detection network to detect targets in the image. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed. Firstly, during the training stage, GCANet is used as the supervisory network for YOLOX, which promotes the extraction of clean information by YOLOX in foggy scenes. During the testing stage, only the YOLOX branch is activated to ensure the detection speed of the model. Secondly, the deformable convolution module is used to improve the gated context aggregation network (GCANet), enhancing the model's ability to capture details of non-homogeneous fog. Finally, we introduce the Coordinate Attention mechanism into the Vision Transformer and redesign the backbone network of YOLOX to enhance the network's feature extraction capability for deep semantic information. The mAP of DSMCT reached 86.56% on the artificially hazy dataset (FOG_VOC), and it reached 62.39% on the real hazy dataset (RTTS). This is respectively 2.27% and 4.41% higher than the current state-of-the-art detection models. The experiments show that the improved DSMCT network has high practicality and effectiveness for object detection in real hazy scenes.
Mask Refined Deep Fusion Network With Dynamic Memory for Robust RGBT Tracking

In the field of object t racking, single RGB-based trackers are insufficient to cope with various extreme environments. Therefore, using complementary bimodal inputs of RGB and thermal infrared information in RGBT tracking has become increasingly popular. However, common RGBT trackers still face challenges in improving the efficiency of bimodal utilization and long-term sequence tracking. This paper proposes a novel network that integrates spatial and temporal information to perform robust RGBT tracking. Specifically, a three-branch backbone network is designed to extract shallow fused features with low complexity. To address the problem of insufficient modal fusion and noise, a deep fusion module is designed to enhance the utilization of common and complementary information between modalities. In addition, a memory module is designed to use temporal information dynamically, reducing tracking failure when occlusions occur. Finally, we proposed a mask refinement module to obtain pixel-level boundary information, reducing the probability of bounding box drift and achieving more accurate scale estimation of the target. Experimental results show that our tracker achieves highly competitive results in terms of performance and speed on multiple RGBT tracking datasets.
Self-Supervised Image Denoising with Subsampling and Residual Learning

Image denoising based on deep learning has made more extensive development by using a large amount of data for network training, however, it is difficult to obtain clean images without noise in actual scenarios, leading to the emergence of self-supervised deep learning technique. In this step, we raise two questions: how to improve the performance of self-supervised learning and how adaptive is the self-supervised method to various networks. In response to these issues, we propose a self-supervised image denoising scheme which generates image training pairs by subsampling the noisy image twice, and combines U-Net and ResNet to form an effective denoising network. As a result, we improve the denoising performance of the latest self-supervised method Neighbor2Neighbor by building a deeper convolutional network while avoiding gradient explosion through careful setting of training parameters. The experimental results show that the proposed method improves the PSNR value by 0.3 dB and SSIM value by 0.004 on average compared to the original algorithm, and also verifies the robustness of the training strategy of Neighbor2Neighbor algorithm to different network structures.
Conformer Based End-to-End ASR System with a New Feature Fusion

In recent years, the proliferation and refinement of neural networks have engendered pioneering advancements in several domains that rely on machine learning and deep learning techniques. Neural network technology has also brought about significant advancements in the field of speech recognition. The current neural networks in various fields are dominated by transformer and conformer. This study explores the current mainstream frameworks and focuses on the research of the conformer network architecture, providing a brief description of its structure. In addition to investigating the mainstream frameworks, the authors discovered that some small plug-and-play attention mechanism frameworks introduced in previous years still yield impressive results, such as ResNet, SeNet, and others. Subsequently, the authors took a closer look at these small plug-and-play frameworks and finally selected resnet as the innovation point. Based on these two, a novel feature fusion framework, the res-Conv model, is proposed. In order to verify the effectiveness of the model, the authors applied the model to the current mainstream open-source speech recognition tool wenet, using the dataset Aishell-1 and LibriSpeech. After continuous debugging, the results show that the proposed res-Conv model, compared to the baseline, achieved a reduction in Character Error Rate (CER) of 0.71-0.82% on the Aishell dataset and a reduction in Word Error Rate (WER) of 0.47-0.53% on the LibriSpeech dataset, while almost maintaining the same number of parameters.
Graph-Based Global Interaction Network with Assistant Prediction for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) has received growing interest in recent years, which aims to extract all emotion clauses and corresponding cause clauses in a document. Previous methods first generate emotion and cause features separately and then generate pair features for pair extraction. These methods either fail to consider the relation between emotion and cause features encoder, or ignore the different importance of different information, resulting in unbalanced information in features, further leading to the wrong prediction. Besides, the original semantic information of clauses is ignored. In this paper, we propose a novel Graph-Based Global Interaction Network with Assistant Prediction (GGINAP) to address the problem. Our model can be divided into two stages: independent prediction and interactive prediction. In the first stage, independent pair features are generated from original clause representations for independent pair prediction. In the second stage, we define various types of meta-paths and apply Heterogeneous graph Attention Network (HAN) to model the global interaction between clauses and pairs, capturing contextual information and causal information simultaneously. Then interactive pair features are obtained for interactive pair prediction. To balance the negative effect of interaction, independent and interactive prediction are both considered for the final prediction. Extensive experiments demonstrate that our model outperforms existing methods and further analysis prove the effectiveness of our framework. Our code is available at https://github.com/klkkkkk/GGINAP.
Part-based Person Re-Identification with Swin Transformer

Person re-identification(Re-ID) is a crucial task in computer vision, which aims to match pedestrian images captured in non-overlapping camera views. It has significant implications for public safety applications. However, current methods primarily rely on global features and thus are vulnerable to background clutters and occlusion. To address these issues, this paper proposes a part-based Re-ID method using the Swin Transformer. In this method, we first utilize human pose estimation method to detect the body keypoints of a pedestrian. We then extract local windows and patches centered at these keypoints, so as to minimize the involvement of background and occlusion information in the down-stream re-identification. These body parts in pose guided windows are fed to the Swin Transformer network. The hierarchical structure of Swin Transformer also enables the proposed method more robust to multi-scale problems. Our experimental results on both holistic and occluded datasets, i.e., Market-1501, DukeMTMC-reID, and Occluded-Duke, demonstrate that the proposed method is on par with the state-of-the-art methods.
Deep Ranking for COVID-19 Community QA

With the rapid acceleration of technological advancements in recent years, society has seen a growing interconnection through the internet. Questions, issues, and confusion spread at a rapid pace, oftentimes inundating professional news outlets and clinical research, which cannot keep up and consequently neglect issues that are new, unknown, or relatively niche. For societal micro-shift trends and social or creative questions, community-based question and answer platforms such as Quora, Reddit, and social media platforms like Instagram and Twitter are far more effective. However, they pose the critical flaw of disorganization and difficulty of user navigation; users must scour through countless repetitive questions and unrelated answers in order to find accurate and appropriate responses to their inquiries. In this paper, a community-based question and answer relevancy ranking system has been developed via advanced deep machine-learning architecture. With its ability to accurately retrieve the most relevant answers to a user-given question, our model greatly assists users with navigating community question and answer platforms with much more ease. Altogether, it significantly improves the accuracy and effectiveness of community-based question and answer platforms. Text data from the COVID-19 Open Research Dataset (CORD-19) [15] was cleaned and incorporated to train our model, which consists of a novel DeepCross Network with embedding features as input, designed with a special dual architecture utilizing both Cross Networks and Deep Networks. Through extensive offline experimentation, we tuned and refined various parameters to validate our model’s feasibility and demonstrate significant improvements in efficiency. Ultimately, our model achieved 97% AUC (Area Under the Curve), a significant upgrade compared to the 52% baseline.