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Contrastive Enhanced Filter Model Based on Bidirectional Transformer for Sequential Recommendation 5

Contrastive Enhanced Filter Model Based on Bidirectional Tra...

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Sun, Anpeng Lu, Jun Heilongjiang University College of Computer Science and Technology Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9798350374407

Transformer-based sequential recommendation is highly powerful as it can capture short-term and long-term sequential recommendation. It plays a crucial role in personalized recommendation systems, aiming to extract dynamic and diverse preference information from users' realtime growing behaviors. This is mainly due to its unique selfattention mechanism, which utilizes item-item interactions within the sequences. However, in online platforms, due to the interweaving of items in user behavior, user behavior data inevitably contains noise. Currently, most methods model user preferences in the time domain, overlooking the influence of frequency patterns on user behavior. To address this issue, this paper shifts the perspective to the frequency domain and proposes a novel contrast-enhanced filtering model for sequential recommendation based on the bidirectional Transformer, called CEFRec. Specifically, the paper designs a filtering layer on top of the bidirectional Transformer, allowing learnable filters to operate on different layers' spectra. This enables the model to capture various frequency patterns and adaptively attenuate noise information within the frequency domain. Next, the model combines the cloze task mask and dropout masks to generate high-quality positive samples and utilizes contrastive learning to enhance the quality of user embeddings learned from the frequency domain. Finally, experimental results on three public benchmark datasets demonstrate that the CEFRec model outperforms state-of-theart sequential recommendation models. © 2024 IEEE.

关键词： Contrastive Learning

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BIVD: Improved Side Adapter Network based on BiFormer 5

BIVD: Improved Side Adapter Network based on BiFormer

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Hu, Liyuan Lu, Jun Heilongjiang University College of Computer Science and Technology Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9798350374407

Currently, most open vocabulary models rely on CLIP (Contrastive Language-Image Pre-training) to classify masked regions, but CLIP models still have many drawbacks. The traditional transformer model is context aware through self attention, but this local context based mechanism is limited by the sequence length of information, it can also impact model effect. Inspired by the article BiFormer, This article replaces the traditional transformer model in the Side Adapter Network (SAN) model with a variant module BIVD block based on BiFormer blocks. This model follows the latest transformer model and extracts features using a four layer feature pyramid structure, For the reduced spatial resolution caused by patch merging, add a Visual-Decoder to reshape the visual vector sequence into a spatial feature map, and restore it to the original resolution through convolution and upsampling. This can enhance the extracted feature information through a feature pyramid without affecting the fusion with CLIP layer features. But the cost is increasing the cost of the model. This article was tested on 5 commonly used datasets and showed a stable improvement in accuracy. © 2024 IEEE.

关键词： BiFormer CLIP component side adapter Visual Decoder

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A New Feature Fusion Method Based on Pre-Training Model for Sequence Labeling 6

A New Feature Fusion Method Based on Pre-Training Model for ...

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6th International Conference on Data Storage and Data Engineering, DSDE 2023

作者： Yu, Shichuan Yang, Yan School of Computer Science and Technology Heilongjiang University Harbin China Heilongjiang University Heilongjiang Key Laboratory of Database and Parallel Computing Harbin China

ISBN: (纸本)9798350333145

To fuse vocabulary features into the pre-training model is the mainstream data feature processing method for sequence labelling tasks. In general, the feature fusion methods that have been proposed at present are direct fusion outside the pre-training model or fusion of lexical features using attention mechanism. However, the study found that this way of vocabulary enhancement does not conform to the word formation rules of modern Chinese. In the Chinese language, it is easy to fuse irrelevant or even incorrect lexical features into the sequence using the above feature processing methods, which is bad for the experimental results of the Chinese sequence labelling task. To solve these problems, we propose to use Cosine Similarity Adapter to process lexical features in Chinese sequence labelling tasks. CSBERT is a hybrid model using this structure based on BERT, which conforms to the word formation rules of modern Chinese to a certain extent. It can fuse the features of the word into the character or eliminate the features of the word in the character according to the cosine similarity between the character vector and a word vector. The experimental results show that CSBERT has better ability to label Chinese sequences than the benchmark model. CSBERT has achieved the best experimental results such as F1-Score on 7 open datasets and the best ability of multi-label classification, which proves that the model has good practical value. © 2023 IEEE.

关键词： Classification (of information)

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Human Pose Estimation Network Based On MultiHeatmap Fusion 5

Human Pose Estimation Network Based On MultiHeatmap Fusion

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Zhao, Yangguang Lu, Jun College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computingof Heilongjiang Province Haerbin China

ISBN: (纸本)9798350374407

Estimating human pose in complex multi-frame situations is a challenging task and has attracted intensive research by many researchers. Although 3D human pose estimation methods have achieved remarkable results in scenes based on single images, their performance often fails once these model transformations are applied to video sequences. Common problems with these models include inability to cope with motion blur, out-of-focus videos, and occlusion of human poses. In order to solve the above problems, this paper proposes a feature extraction and representation model MHMF, which is used in the feature extraction stage of the model. The initial features extracted by the backbone network HRNet-w32 are guided by the heat map to the attention layer, which improves the network's attention to important areas for predicting key points of human posture. At the same time, the integration of the aggregation heat map and the backbone network heat map improves the spatiotemporal consistency of the key points. In addition, to improve the accuracy of mesh pose estimation under occlusion, this paper proposes a transformer-based NewDSTformer model. By adjusting the structure of the Transformer encoder, increasing the encoder level and combining it with the dynamic progressive attention masking method. The model can adapt to different input situations, handle the positional relationship of local key points, and be able to perform accurate detection even under occlusion. It was evaluated on the 3DPW data set and improved the accuracy by $0.3 \%$, indicating that this paper effectively improved the performance of 3D human mesh reconstruction. © 2024 IEEE.

关键词： Mesh generation

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YOLO-RSOD: Improved YOLO Remote Sensing Object Detection 27th

YOLO-RSOD: Improved YOLO Remote Sensing Object Detection

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Yang Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031784460

Remote sensing object detection has important application value in fields such as environmental monitoring and resource detection and analysis. However, the current universal object detectors are not very effective in detecting remote sensing objects. To this end, this paper proposes an efficient, low-complexity and anchor-free remote sensing object detection framework YOLO-RSOD based on YOLOv7. First, an additional Tiny Object Head is proposed for better detection of micro-remote sensing objects. The original Head is then replaced with Decoupled Head (DH) to explore the detection potential of the decoupled detection head structure. Then the Explicit Vision Center (EVC) in the Centralized Feature Pyramid Network (CFP) is added to further improve the detection ability of remote sensing objects. Finally, this article also integrates a global attention module (GAM) to find attention areas in dense object scenes. Ablation experiments on the general remote sensing target detection dataset VisDrone2021 demonstrate the effectiveness of several modules introduced in this paper in remote sensing target detection. On the VisDrone2021 data set, YOLO-RSOD can achieve accuracy rates of 30.7% AP50:95 and 51.7% AP50, which are 3.1% and 3.2% higher than the baseline model respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object tracking

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False Positive Detection for Text-Based Person Retrieval 21st

False Positive Detection for Text-Based Person Retrieval

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Cao, Yan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9789819601189

Text-based person retrieval (TBPR) is a challenging topic in cross-modal retrieval tasks, aiming to query corresponding person images based on textual descriptions. This task is complicated by noisy correspondences between images and text due to incorrect text annotations and low-quality images. Although many robust noise learning methods have been proposed with satisfactory performance, they tend to directly filter out a portion of the noise in the dataset, which decreases the ability to distinguish the noise as the number of training epochs increases. This paper proposes a noise-robust learning strategy based on false positive detection (FPD). Precisely, FPD consists of two main components: 1) The key-value library (KVL) module utilizes a set of clean samples, assigns confidence weights to each training sample during the training process, and adaptively adjusts the contribution of each sample to reduce the accumulation of noise. 2) To improve the performance of TBPR, this paper designs a local token-based selection method, which utilizes the contribution of each local token to the weights in the self-attention matrix and selects the local tokens with vital information as global representations. Extensive experiments are conducted on three datasets (CUHK-PEDES, ICFG-PEDES, and RSTPReID) to evaluate the performance and robustness of FPD under noisy conditions. The experimental results show that the FPD has better results at high noise ratios, and all the metrics outperform the state-of-the-art method under the RSTPReID dataset with a noise ratio of 80%, where Rank-1 outperforms the best method by 2.28%, 1.64% for mAP, and 1.92% for mINP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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DCI-Net: Remote Sensing Image-Based Object Detector 27th

DCI-Net: Remote Sensing Image-Based Object Detector

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cui, Quanyue Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781124

Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring the contextual information of the object, while the existence of multi-scale and high image resolution of objects in remote sensing images also affects the accuracy of the object detection task. Based on the above problems, this paper proposes a object detector DCI-Net (Dynamic Context-Aware IoU Network) based on remote sensing images, in which the proposed CASK (Context-Aware Selective Kernel) module can explicitly model the interdependence between the convolutional feature channels. A loss function Pi_IoU is proposed, which adaptively adjusts the penalty factor in combination with the size of the detected object. A DySample module is introduced, which is able to effectively extract and utilize the spatial structure features. The model in this paper improves the detection accuracy of complex objects in remote sensing images. On the DIOR dataset, compared with the baseline model YOLOV9, the accuracy is improved by 0.6%, the number of parameters is decreased by 4%, and the floating point operation speed is improved by 27.8%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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Cross-Modality Encoder Representations Based On External Attention Mechanism 3

Cross-Modality Encoder Representations Based On External Att...

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3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Zheng, Yudong Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin China College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9798350335972

With the prevalence of deep learning, people use multi-modality information for interpretation and reasoning. In this paper, a cross-modality encoder CMEEA (cross-modality encoder representation based on external attention mechanism) is adopted to improve the accuracy of the model. The two external storage units of CMEEA can be regarded as a dictionary of the whole dataset to improve the performance of the network, while learning more representative features of the input and reducing the computational cost. This paper uses five pretraining tasks that help the model learn internal modal and cross-modal relationships. It also verifies the generalizability of the model by applying the pretraining cross-modal model to the visual inference task NLVR2, and improves the previous results by 0.1%. By fine-tuning the pretrain parameters, the model improves by 1.3% on visual question answering (VQA). © 2023 IEEE.

关键词： Deep learning

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A Weakly Supervised Semantic Segmentation Model with Enhanced CLIP Feature Extraction

A Weakly Supervised Semantic Segmentation Model with Enhance...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kong, Fanxuan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9798350368741

This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model's image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve the performance of the Weakly Supervised Semantic Segmentation (WSSS) task. WSSS-ECFE employs the Enhanced Bottleneck module proposed in this paper and adds dynamic residual connection to improve the model's processing effect on complex scenes. In terms of implementation, the Enhanced Bottleneck module employs the Swish activation function and the Depthwise Separable Convolution to enhance the feature extraction and segmentation capability of the model, and uses multiple attention mechanisms to further optimize the feature representation and segmentation accuracy. The WSSS task on the public datasets PASCAL VOC 2012 and MS COCO 2014 achieves 82.6% and 56.3% mean intersection over union (mIoU), achieving state-of-the-art performance in models with low resource requirements. © 2025 IEEE.

关键词： Attention Mechanism Bottleneck Feature Extraction Residual Connection Weakly Supervised Semantic Segmentation (WSSS)

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Research on parallel Algorithms of Uncertain Combinational Coding 10

Research on Parallel Algorithms of Uncertain Combinational C...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Song, Xuanting Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin China Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

In this era of rapid development of information science and technology, data coding technology has been widely used in multimedia, computer, communication and other fields. Non-deterministic combined coding is further explored in deterministic combined coding. It is a kind of lossless coding. In combinatorial mathematics, the operation of the combination number calculates the ordinal number of the sequence to be coded. On the basis of the deterministic combination coding, the adjacent identical elements are eliminated and processed separately, which reduces the length of the ordinal number. At the same time, the reference sequence is encoded to further save space. However, the ordinal calculation of the non-deterministic combination coding is still a complicated process, and the coding speed is still slow. This article uses openMP technology to parallelize the ordinal calculation part of the non-deterministic combination coding. The running time of coded ordinal on the eight-core CPU is 23.38% of the original, which is 4.28 times optimized, and the overall coding time efficiency of the 17 segmented file instances has been improved about 2.94 times. It indicates the effectiveness of the parallel strategy in this article. Finally, this article analyzes the experimental results. © 2022 IEEE.

关键词： Number theory

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