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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Jinxin, Deng Xizhong, Qin Rong, Yang Xiaoyi, Lv Xinjiang University of China School of Information Science and Engineering Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Information the Fifth Affiliated Hospital of Xinjiang Medical Urumqi830017 China

ISBN: (纸本)9798350324303

Relational triple extraction from unstructured text, which is in form of (subject, relation, object), is crucial in information extraction and knowledge graph construction. However, most existing methods still suffer from some limitations, such as error propagation, redundancy of relation prediction, and poor generalization. To overcome these deficiencies, we propose a relation-first bidirectional cascade multi-Task framework for relational triple extraction. This framework solves the relational redundancy problem by relation prediction, alleviates error propagation through a complementary bidirectional entity tagger, and uses multi-Task learning to improve the generalization of the model. Concretely, we first use a relation detection component to predict potential relations in sentences and conduct the following entity pairs extraction based on specific relations. Then, we extract entity pairs from two paralleled directions. Meanwhile, entity recognition is used as an auxiliary task, improving generalization capability. Extensive experiments were conducted on the NYT and WebNLG datasets We evaluate our model on four public datasets to demonstrate the effectiveness of our proposed RFBiCF framework, even in the complex scenario of overlapping triples. © 2023 IEEE.

关键词： Forecasting

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Multi-Task Model Based on Vision Task Level for Saliency Object detection in Foggy Conditions

Multi-Task Model Based on Vision Task Level for Saliency Obj...

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IEEE International Conference on Image processing

作者： Yusen Zhu Gang Zhou Jingxu Ren Jiakun Tian Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

In recent years, saliency object detection methods based on convolutional neural networks have been widely studied, and have achieved excellent performance in clear images. However, due to the low visibility of images in foggy conditions, the existing saliency object detection methods will be seriously affected or even ineffective. To address this problem, we introduce an end-to-end multi-task learning network. We design two subetworks for depth estimation and image restoration as auxiliary tasks to improve saliency object detection in foggy conditions. According to different characteristics of vision tasks, different shared layers are assigned to improve the performance of saliency object detection. Experiments show that our method has been greatly improved on both synthetic foggy datasets and real-to-world foggy datasets, outperforming many state-to-the-art saliency object detection methods.

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Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections

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Digital Communications and Networks 2024年第6期10卷 1600-1610页

作者： Ziyi Lu Tianxiong Wu Jinshan Su Yunting Xu Bo Qian Tianqi Zhang Haibo Zhou Key Laboratory of Vibration Signal Capture and Intelligent Processing Yili Normal UniversityXinjiang835000China School of Electronic Science and Engineering Nanjing UniversityNanjing210023China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen518000China

With the support of Vehicle-to-Everything(V2X)technology and computing power networks,the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as *** to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research *** paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional,and designs an efficient communication system and ***,by analyzing the collision point occupation time,this paper formulates an absolute value programming ***,this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time(EI-OET)algorithm based on edge-cloud computing power ***,the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular ***,simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms,and the experimental results demonstrate its high efficiency,low latency,and low complexity.

关键词： Unsignalized intersection Automatic vehicle scheduling Edge computing Communication protocol Computing power network

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Research on Shaking Video Image Stabilization Algorithm for Moving Object detection

Research on Shaking Video Image Stabilization Algorithm for ...

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Algorithm, Image processing and Machine Vision (AIPMV), International Conference on

作者： Chuangchuang Wang Zhenhong Jia Xiaohui Huang Sensen Song Jiajia Wang Gang Zhou Fei Shi Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local Image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm.

关键词： Wavelet transforms Accuracy Surveillance Search methods Object detection Jitter Streaming media Feature extraction Real-time systems Gaussian mixture model

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A Lightweight Speech Emotion Recognition Model with Bias-Focal Loss

A Lightweight Speech Emotion Recognition Model with Bias-Foc...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Shijing Hou Huamin Yang Ying Hu Key Laboratory of signal detection and processing Xinjiang University Urumqi China

A major challenge in speech emotion recognition (SER) is how to build a lightweight model with limited training data for applying on the devices with limited in resources. In this paper, we propose a lightweight speech emotion recognition (SER) model with Bias-Focal loss function, where a Dynamic Separable Convolution (DySC) block is designed to extract more fine-grained emotional features and makes the model smaller. We propose a Bias-Focal loss to address the issue of the inconsistent of training samples, while focusing on data points with high feature diversity during the training phase. Experimental results show that our proposed lightweight model is the smallest comparing with other methods, while the number of parameters in our proposed model is 0.85M. Meanwhile, our proposed model achieves the best performance that the score of Unweighted Accuracy (UA) is 75.01 %, and that of Weighted Accuracy (WA) and F1-score are 74.05% and 74.29 % on the IEMOCAP (scripted+improvised)dataset.

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Beyond the Snowfall: Enhancing Snowy Day Object detection Through Progressive Restoration and Multi-Feature Fusion

Beyond the Snowfall: Enhancing Snowy Day Object Detection Th...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhong Wang Gang Zhou Jing Ma Tianhao Xue Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

In the field of computer vision, object detection is a prominent and challenging task. Despite the favorable performance of deep learning-based object detection techniques on clear images, it fails in inclement weather conditions like snow because of image degradation. Recent efforts have explored using image restoration methods to enhance degraded images before object detection. However, direct restoration can sometimes cause new disturbances, impeding detection performance improvements. To address this issue, we propose a joint framework that connects the iterative desnow module and detection module in an end-to-end manner. Specially, we design an Advantage Union structure for multi-feature fusion, which effectively combines original, intermediate, and restored features, reducing potential information loss from restoration. Experimental results show that our method achieves higher accuracy compared to the recent state-of-the-art methods in both synthetic dataset and real-to-world snowy images.

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RVDNet: A Two-Stage Network for Real-World Video Desnowing with Domain Adaptation

RVDNet: A Two-Stage Network for Real-World Video Desnowing w...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Tianhao Xue Gang Zhou Runlin He Zhong Wang Juan Chen Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

Video snow removal is an important task in computer vision, as the snowflakes in videos reduce visibility and negatively affect the performance of outdoor visual systems. However, due to the complexity of real snowy scenarios, it is difficult to apply existing supervised learning-based methods to process real-world snowy videos. In this paper, we propose a novel two-stage video desnow network for the real world, called RVDNet. The first stage of RVDNet utilizes Spatial Feature Extraction Modules (SFEM) to extract the spatial features of the input frames. In the second stage, we design Spatial-Temporal Desnowing Modules (STDM) to remove snowflakes via spatio-temporal learning. Furthermore, we introduce the unsupervised domain adaptation module, which is embedded for aligning the feature space of real and synthetic data in the spatial and spatio-temporal domains, respectively. Experiments on the proposed SnowScape dataset prove that our method has superior desnow performance not only on synthetic data, but also in the real world.

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Unsupervised Domain Adaptive Learning for Image Desnowing with Real-World Data

Unsupervised Domain Adaptive Learning for Image Desnowing wi...

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IEEE International Conference on Image processing

作者： Jingxu Ren Gang Zhou Yusen Zhu Yangxin Liu Juan Chen Zhenhong Jia Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.

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Improved Self-Consistency Training with Selective Feature Fusion for Sound Event detection

Improved Self-Consistency Training with Selective Feature Fu...

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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Mingyu Wang Yunlong Li Ying Hu Key Laboratory of signal detection and processing Xinjiang University Urumqi China

Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for semi-supervised SED based on Mean Teacher (MT) method. For teacher and student models, each adopts two branches with the same CRNN structure, the two branches help training the model by means of consistency regularization. ISCT strategy incorporates self-consistency loss on the basis of MT loss to improve the generalization performance of the model. A selective feature fusion (SFF) module is designed for applying in the shallow layers of the feature extraction part to selectively fuse the features with different scales. A parallel attention (PA) module is designed for applying in the deep layers of the feature extraction part to obtain much richer high-level features by the channel and spatial-wise attention. Ablation experiments verify the effectiveness of our proposed ISCT strategy, SFF and PA modules. In addition, compared with four methods, our proposed method achieves competitive performance on the DCASE 2020 task4 dataset.

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Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network 3

Exploring Turkish Speech Recognition via Hybrid CTC/Attentio...

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3rd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Yolwas, Nurmement Ren, Zeyu Wang, Huiru Slamu, Wushour Xinjiang University Xinjiang Key Laboratory of Signal Detection College of Information Science and Engineering Urumqi China Xinjiang University Xinjiang Multilingual Info. Technology Laboratory College of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350302356

In recent years, there has been rapid development in deep learning-based End-To-End speech recognition technology. However, the performance of Turkish speech recognition systems has been hindered by the lack of Turkish speech data. To address this, this paper focuses on several speech recognition tuning techniques. Experimental results demonstrate that the best performance is achieved when utilizing a combination of speed perturbation and noise addition for data augmentation, along with the beam search width set to 16. Furthermore, to maximize the utilization of effective feature information and enhance feature extraction accuracy, a novel feature extractor called LSPC is proposed. By combining LSPC with the LiGRU network, a shared encoder structure is formed, and model compression is achieved. The results indicate that when using only Fbank features, the performance of LSPC surpasses that of MSPC and VGGnet, resulting in a respective improvement of 1.01% and 2.53% in the word error rate (WER). Finally, building upon the aforementioned advancements, a new multifeature fusion network is proposed as the primary encoder structure. The results demonstrate that the WER of the proposed feature fusion network, based on LSPC, further improves by 0.82% and 1.94% when compared to single-feature extraction using LSPC, specifically utilizing Fbank features and spectrogram features. As a result, our model achieves comparable performance to advanced End-To-End models. © 2023 IEEE.

关键词： Speech recognition

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