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DMSA: Dynamic Multi-Scale Unsupervised Semantic Segmentation Based On Adaptive Affinity 48

DMSA: Dynamic Multi-Scale Unsupervised Semantic Segmentation...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Kun Lu, Jun Heilongjiang University College of Computer Science and Technology Harbin150080 China Jiaxiang Industrial Technology Research Institute of Hlju Jining China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781728163277

The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-scale context information. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced, which can adaptively refine the initial pseudo labels after feature fusion to obtain high quality pseudo labels. Experiments show that the proposed DSMA framework is superior to the existing methods on the saliency dataset. On the COCO 80 dataset, the MIoU is improved by 2.0, and the accuracy is improved by 5.39. On the Pascal VOC 2012 Augmented dataset, the MIoU is improved by 4.9, and the accuracy is improved by 3.4. In addition, the convergence speed of the model is also greatly improved after the introduction of the PAR module. © 2023 IEEE.

关键词： Semantic Segmentation

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YOLO-SOD: Improved YOLO Small Object Detection 21st

YOLO-SOD: Improved YOLO Small Object Detection

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

作者： Xu, Yang Lu, Jun Wang, Chao College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Province Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China Wuhan University Hubei Province Wuhan430072 China

ISBN: (纸本)9789819601240

Small object detection has important application value in the fields of environmental monitoring, resource detection and analysis, etc. However, the current general object detectors are not very ideal for the detection of small objects. To this end, this paper proposes an efficient, low-complexity, anchor-free small object detection framework YOLO-SOD based on YOLOv8. First, a content-aware feature recombination upsampling operator (CARAFE) is integrated in the upsampling operation part of the framework to achieve more accurate and efficient feature reconstruction. Then, the spatial and channel reconstruction convolutional block (SCConv) is widely integrated in BackBone and Neck to reduce the computational cost caused by redundant feature extraction in visual tasks. Finally, the occlusion-aware attention module (SEAM) is introduced at the end of the detection framework to help the model more accurately identify occluded objects. The ablation experiment on the general small object detection dataset VisDrone2021 proves the effectiveness of several modules introduced in this paper for small object detection. On the VisDrone2021 dataset, YOLO-SOD can achieve an accuracy of 31.1% AP50:95 and 52.3% AP50, which are 2.5% and 3.1% higher than the baseline model respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

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Cross-level Relational Graph Contrastive Learning for Bundle Recommendation

Cross-level Relational Graph Contrastive Learning for Bundle...

引用

2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Nan Sun, Jiabao Li, Jinbao College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang University Harbin China Qilu University of Technology Shandong Artificial Intelligence Institute School of Mathematics and Statistics Jinan China

ISBN: (纸本)9798350304855

Different from the traditional recommendation system which regards a single item as an atomic unit, the purpose of bundle recommendation is to recommend a set of items to users. In recent years, some work often uses the model based on GNN to solve the bundle recommendation problem. However, compared with the traditional recommendation models, the GNN-based bundle recommendation has a more serious data sparse problem, which significantly reduces the performance of the model. Meanwhile, due to the lack of modeling ability of complex interactive information and dependent information, it is still far from enough to learn the representation of users and bundles. In view of the advantages of contrast learning in mining its own supervisory signals, we propose a model named Cross-level relational graph Contrastive Learning for Bundle Recommendation (CL2BRec). Different from previous contrastive learning methods, our method learns representations of users and bundles from multiple relational graphs of different levels through a crossview contrastive learning paradigm. Specifically, we regard the user-item/bundle graph as a local interaction relation view, the bundle-item graph as a local dependency relation view, and the user-bundle-item tripartite graph as a global structure view to build a cross-level bundle recommendation model. Extensive experiments conducted on two benchmark datasets demonstrate that CL2BRec outperforms the state-of-the-art methods by over 12.56% and 8.6%, respectively. © 2023 IEEE.

关键词： Recommender systems

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A Bidirectional Representation Recommendation Based On Relevance Information Enhancement To Solve User Interest Drift

A Bidirectional Representation Recommendation Based On Relev...

引用

2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Nan Li, Kun Jiang, Xuefeng Li, Jinbao College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China Qilu University of Technology Shandong Artificial Intelligence Institute School of Mathematics and Statistics Jinan China

ISBN: (纸本)9798350304855

The phenomenon that user interests change dynamically over time is called user interest drift, which makes it difficult for recommendation systems to obtain users' real interests. In order to better provide users with personalized services, it is very important to study recommendation methods that can be applied to user interest drift. However, the new interests of users are often very different from the interests that users have interacted with historically. It makes the correlation between the original user's historical interaction and the user's new interest very weak. Meanwhile, the information contained in user's sessions is not enough to support the inference of the new interest, resulting in a serious decrease in model performance. To alleviate the above problem, we propose a bidirectional representation recommendation based on relevance information enhancement to solve user interest drift (RA-Bert). The model is based on Bert and uses bidirectional self-attention mechanism to represent user sessions. In this model, we design a relevance information enhancement module to adaptively capture the information associated with the user's new interest from neighbors' sessions. Then, we integrate the relevance information into user's history sessions, which can effectively enhance the correlation between user's history sessions and the new interest. Further, in order to effectively supplement the missing relevance information in users' sessions, we design a new attention unit, which explicitly models the difference information between users and neighbors. Extensive experiments have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://***/LucasZen/paper_code. © 2023 IEEE.

关键词： Recommender systems

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Double Loss Block Neural Architecture Search 10

Double Loss Block Neural Architecture Search

引用

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

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

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Neural architecture search aims to automatically design network architectures by machines, which is expected to bring about a new revolution in machine learning. Despite high expectations, the effectiveness and efficiency of existing neural architecture search solutions are unclear, and the inefficiency of neural architecture search solutions is mainly attributed to inaccurate architecture evaluation. In this work, this paper divides the large search space of neural architecture search into multiple modules, effectively reducing the error caused by shared parameters to ensure that the candidate architectures in the modules are fully trained. By adopting a block search method, this paper can Evaluate all candidate architectures in a module, addressing the problem of inaccurate evaluation. In addition, high-quality data is necessary. Under the guidance of the teacher model, combined with the ground truth, the neural architecture search is supervised by the double loss function. Under the constraint of the same amount of computation, the method in this paper achieves 77.3% top-level accuracy on ImageNet., which is 1.0% higher than the efficient network EfficientNet-B0, and 0.3% higher than the DNA method without additional computation. Therefore, the double loss block neural architecture search model proposed in this paper significantly improves the effectiveness of neural architecture search. © 2022 IEEE.

关键词： Distillation

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Session-Recommendation Based on Gated Neural Network to Extract Item Structure Feature Graph 22

Session-Recommendation Based on Gated Neural Network to Extr...

引用

5th International Conference on Data Storage and Data Engineering, DSDE 2022

作者： Zhang, Wei Yang, Yan Zhong, Yingli College of Computer Science and Technology Heilongjiang University China Heilongjiang Key Laboratory of Database and Parallel Computing Heilongjiang University China School of Computer Science and Technology Heilongjiang University China

ISBN: (纸本)9781450395724

The issue of inaccurate user portrait recommendations for short-Term conversations is solved. The classical recommendation is based on an assumption: The user's historical behavior can represent the user's stable long-Term preferences, and the result is generated based on the user's historical behavior. However, there is sparseness in the user's historical behavior information record in the actual data set, and the obtained data cannot accurately represent the user's preferences. Research on a session recommendation focuses on user behavior data in the short term, and aims at predict anonymous user behavior based on in-session behavior information. It plays a vital role in application scenarios where user behavior information is scarce. Feature extraction is performed on the user's anonymous behavior information graph generated in a fixed period of time as the embedding representation of user's interest preference;the graph structure of the same item in the global click interaction graph is combined with the input a gated graph neural network as the item attribute embedding representation. The prediction part adopts simulated interactive calculation to produce results. With the help of gated graph neural network, features extraction and analysis of data in short-Term sessions: session-based user behavior data and in-session item attributes are obtained from the graph data structure, which reduces errors caused by inconsistent data processing methods. The user's current interest is captured based on the behavior information in the session, the global inter-item conversion graph is generated based on the sequence of click moments, and the feature vector is obtained through graph neural network processing. The generation of recommendation results is more time-sensitive, complete the anonymity problem due to the scarcity of user's information, and improves the accuracy of recommendations. © 2022 ACM.

关键词： Graph neural networks

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Contrastive Enhanced Filter Model Based on Bidirectional Transformer for Sequential Recommendation

Contrastive Enhanced Filter Model Based on Bidirectional Tra...

引用

Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

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

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

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.

关键词： Adaptation models Time-frequency analysis Electric potential Filters Image processing Noise Contrastive learning Transformers Time-domain analysis Recommender systems

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ShuffleVITNet: Mobile-Friendly Vision Transformer With Less-Memory

ShuffleVITNet: Mobile-Friendly Vision Transformer With Less-...

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International Joint Conference on Neural Networks (IJCNN)

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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traditional Convolutional Neural Networks (CNNs) can efficiently acquire local features, while ViT uses a Transformer structure that captures global contextual information. In this paper, a new high-performance lightweight target detector is designed by combining the respective advantages of CNN and ViT: ShuffleViTNet. In this paper, we improve the VIT Block in MobileVIT, firstly, we use channel-by-channel convolution to replace the normal convolution to reduce the memory access cost (MAC), number of parameters (Parmas) and floating-point operations (FLOPs) in the model inference. The 1×1 convolution is then used to fuse the local features learned by the convolution with the global features learned by the Transofmer, which further lightens the model and allows for easier modification of the number of channels and migration to other lightweight neural networks. Finally a lightweight network friendly to mobile devices is constructed using the improved VIT Block. On the ImageNet-1k dataset, ShuffleViTNet achieves 75.2% Top-1 accuracy, the model’s floating-point operation is 536.5 MFLOPs, which is 44.0% less compared to MobileViT-XS, and the cost of memory accesses for model inference is saved by about 25.6%, and the required memory is saved by about 66.37%. For the object detection task on the MS-COCO dataset, ShuffleViTNet obtains higher mAP accuracy. The experimental results show that ShuffleViTNet is a high-performance lightweight network and can be well applied to downstream tasks.

关键词： Accuracy Costs Convolution Computational modeling Neural networks Memory management Object detection

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BIVD: Improved Side Adapter Network based on BiFormer

BIVD: Improved Side Adapter Network based on BiFormer

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

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

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

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.

关键词： Adaptation models Vocabulary Visualization Costs Semantic segmentation Feature extraction Transformers Decoding Spatial resolution Context modeling

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Human Pose Estimation Network Based On MultiHeatmap Fusion

Human Pose Estimation Network Based On MultiHeatmap Fusion

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

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

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

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.

关键词： Adaptation models Accuracy Three-dimensional displays Pose estimation Video sequences Feature extraction Transformers Spatiotemporal phenomena Heat maps Image reconstruction

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