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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zhou, Bing Ge, Jian Kang Zuo, Xianyu Key Laboratory of Analysis and Processing of Big Data Henan Kaifeng475004 China School of Computer and Information Engineering Henan University Henan Kaifeng475004 China

ISBN: (纸本)9781510664616

Remote sensing data processing involves many steps. This paper uses process flow to represent these processing steps. This paper combines remote sensing image metadata (RSIM) with case based reasoning (CBR) technology, proposes a parameter value adaptive method based on RSIM, and constructs a remote RSIM case model. It makes the representation of RSIM more intuitive and convenient. This method consists of two parts: case building and case similarity measurement. Case building refers to building RSIM into RSIM cases according to case models, and then classifying and saving each case. Case similarity measurement refers to the similarity measurement of metadata in the production process and each case under the flow. Finally, the parameter values will be configured according to the highest similarity. The method is tested in an actual remote sensing production system, and the experimental results show that the method has good applicability in the parameter value adaptation of remote sensing process production. © 2023 SPIE.

关键词： Remote sensing

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Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated image Detection

arXiv

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arXiv 2024年

作者： Chen, Yingjian Zhang, Lei Niu, Yakun Tan, Lei Chen, Pei Henan Key Laboratory of Big Data Analysis and Processing Henan University China

Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-scale, real-world images. Our findings indicate that: 1) Pre-trained models can cluster the features of real images effectively. 2) Models with pre-trained weights can approximate an optimal generalization solution at a specific training step, but it is extremely unstable. Based on these facts, we propose a simple yet effective training method called Learning on Less (LoL). LoL utilizes a random masking mechanism to constrain the model’s learning of the unique patterns specific to a certain type of diffusion model, allowing it to focus on less image content. This leverages the inherent strengths of pre-trained weights while enabling a more stable approach to optimal generalization, which results in the extraction of a universal feature that differentiates various diffusion-generated images from real images. Extensive experiments on the Genimage benchmark demonstrate the remarkable generalization capability of our proposed LoL. With just 1% training data, LoL significantly outperforms the current state-of-the-art, achieving a 13.6% improvement in average ACC across images generated by eight different models. Copyright © 2024, The Authors. All rights reserved.

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CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Retrieval 26

CLIP-HASH: A Lightweight Hashing Network for Cross-Modal Ret...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Mengying Lai, Hanjiang Yin, Jian Xu, Mengying Sun Yat-Sen University School of Systems Science and Engineering Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350331684

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods. © 2023 IEEE.

关键词： Encoding (symbols)

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Attention based simplified deep residual network for citywide crowd flows prediction

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Frontiers of Computer Science 2021年第2期15卷 51-62页

作者： Genan DAI Xiaoyang HU Youming GE Zhiqing NING Yubao LIU School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the *** practice,emergency applications often require less training ***,there is a little work on how to obtain good prediction performance with less training *** this paper,we propose a simplified deep residual network for our *** using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar ***,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time *** on the real datasets,we construct a series of experiments compared with the existing *** experimental results confirm the efficiency of our proposed methods.

关键词： crowd flows prediction spatio-temporal data mining attention

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A Review of Detection-Related Multiple Object Tracking in Recent Times 26

A Review of Detection-Related Multiple Object Tracking in Re...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Li, Suya Cao, Ying Xie, Xin Henan University School of Computer and Information Engineering Jinming Campus Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Jinming Campus Kaifeng China

ISBN: (纸本)9791188428120

Multi-object tracking (MOT) is garnering more attention due to its widespread application in the area of autonomous driving, human-computer interaction, and intelligent video surveillance. Especially in recent years, MOT has rapidly developed thanks to related technologies such as object detection, which has helped in handling interfering factors such as crowded scene occlusion, small objects, and similar appearances. Among these, Detection-based MOT is the mainstream for accurately forming objects' trajectories. Therefore, according to the analysis of the last three years' research, this paper particularly focuses on discussing the continuous optimization strategies of MOT around the development of object detection at each stage. In addition, this article also introduces the commonly used benchmark datasets and related applications of MOT. © 2024 Global IT Research Institute - GIRI.

关键词： Object detection

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Multimodal Dialogue Generation Based on Transformer and Collaborative Attention 5

Multimodal Dialogue Generation Based on Transformer and Coll...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Guan, Wei Zhang, Zhen Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

In view of the fact that the current multimodal dialogue generation models are based on a single image for question-and-answer dialogue generation, the image information cannot be deeply integrated into the sentences, resulting in the inability to generate semantically coherent, informative visual contextual dialogue responses, which further limits the application of multimodal dialogue generation models in real scenarios. This paper proposes a Deep Collaborative Attention Model (DCAN) method for multimodal dialogue generation tasks. First, the method globally encode the dialogue context and its corresponding visual context information respectively;second, to guide the simultaneous learning of interactions between image and text multimodal representations, after the visual context features are fused with the dialogue context features through the collaborative attention mechanism, the hadamard product is used to fully fuse the multimodal features again to improve the network performance;finally, the fused features are fed into a transformer-based decoder to generate coherent, informative responses. in order to solve the problem of continuous dialogue in multimodal dialogue, the method of this paper uses the OpenVidial2.0 data set to conduct experiments. The results show that the responses generated by this model have higher correlation and diversity than existing comparison models, and it can effectively integrate visual context information. © 2022 ACM.

关键词： image enhancement

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GA-SATIC: Semi-autoregressive Transformer image Captioning Based on Geometric Attention 5

GA-SATIC: Semi-autoregressive Transformer Image Captioning B...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Li, Ziyi Li, Ma Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

image captioning is a cross-domain task involving image and natural language processing. Most of the current models follow an encoder-decoder architecture, where the encoder takes image feature vectors as input, and the decoder uses an autoregressive or non-autoregressive approach for decoding. However, most models extract the feature vectors of an image through region proposals of the object detector, without considering the relative spatial relationship between objects. When decoding, the autoregressive decoding method is adopted, that is, the next word is generated based on the generated word, and the generation is performed step by step, which will lead to high delay in the inference process. To solve this problem, scholars have proposed a non-autoregressive approach, which speeds up inference by generating all words in parallel, but reduces the quality of generated captioning. Aiming at the above problems, this paper proposes a semi-autoregressive Transformer model based on geometric attention. The encoder integrates the relative spatial relationship between the detected objects through geometric attention and appearance attention, so as to enhance spatial awareness;the decoder adopts a semi-autoregressive decoding method, which is serialized globally and paralleled locally, enabling the model to achieve a better trade-off between decoding speed and captioning accuracy. Extensive experiments and ablation studies on the MSCOCO dataset have shown that the model achieves better performance compared to state-of-the-art models. © 2022 ACM.

关键词： Signal encoding

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Visual Question Answering Model Based on CAM and GCN 5

Visual Question Answering Model Based on CAM and GCN

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wen, Ping Li, Ma Zhen, Zhang Ze, Wang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Visual Question Answering (VQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional Network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. image region dependence, and finally, continue to optimize the fused features through feature enhancement. The test results on the VQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models. © 2022 ACM.

关键词： Semantics

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Research on the Generation of Emotional Dialogue Statements in Generative Adversarial Networks 5

Research on the Generation of Emotional Dialogue Statements ...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Guan, Wei Huang, Mingrui Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

With the continuous progress of neural network technology in different fields, people have put forward higher requirements and prospects for the research of deep neural network technology in the field of artificial intelligence, and language intelligence is the core problem of artificial intelligence. As an important research content in the field of natural language processing, dialogue generation has been widely concerned by the academic community for a long time, and this research is also widely used in people's daily lives, such as medical Q&A, e-commerce shopping, emotional response, etc. There are still many problems in the existing model for dialogue generation research, such as easy to produce universal replies ("ok", "emm", etc.), the emotional information of the reply statement is not strong enough, and the response is not related to the subject. For this problem, we propose to use an emotional dialogue generation model based on a generative adversarial network to generate statements with emotional responses, and our network has a generator and multi-level discriminator. In terms of discriminators, multi-level discourse discriminators are introduced to guide the generation of replies and the strengthening of emotional information, to discriminate emotions at the individual word level and sentence level, to solve the problem that the same text may be different in different semantic environments and the emotional information of some words is not clear in some cases, in order to achieve maximum accuracy the emotional discrimination results are fed back to the generator and guide the generation of generator statements. In addition, compared with the traditional generative adversarial network, the task processing ability for large pieces of text is improved, and the model has been shown to produce emotional reply statements with better baseline levels than in the past. © 2022 ACM.

关键词： Generative adversarial networks

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