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International Conference on Signal processing Proceedings (ICSP)

作者： Xinlong Cao Jiaojiao Li Yuanqing Li Jiapeng Yan Zhenxing Zheng School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts & Telecommunications Xi'an China Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi'an China International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province Xi'an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and spatial correlations without considering temporal correlations. To further enhance compression performance, this paper proposes a Transformer-based point-by-point prediction algorithm for auroral spectral images, which simultaneously uses spatial, spectral and temporal contexts for prediction. Our prediction network consists of two parts: an encoder and a decoder. The encoder is composed of the encoding unit of the original Transformer and is used to extract features from spatial, spectral and temporal contexts. The decoder consists of fully connected layers and is used for prediction. Experimental results show that the average bitrate of this method is reduced by 0.179 bpp compared with the JPEG2000 algorithm, and the average bitrate is reduced by 0.054 bpp compared with the online DPCM algorithm.

关键词： Ion radiation effects image coding Correlation Signal processing algorithms Transformers Prediction algorithms Feature extraction Decoding Magnetosphere Compression algorithms

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image-to-Class Metric based on Category Traversal for Few-shot Learning 10

Image-to-Class Metric based on Category Traversal for Few-sh...

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

作者： Yuan, Sihao Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

The purpose of few-shot learning is to learn a classification model with good generalization performance. When there is only one sample or several samples, the model can also be quickly generalized to new categories. In this paper, we use the method based on metric learning to complete the few-shot learning image classification task. In this method, we introduce a feature learning module to improve the representation ability of the feature extraction network. Through category traversal, the module extracts the intra-class common features and inter-class unique features of images. Finally, by comparing the similarity between the input image and the local descriptors of each category, the classification task is completed by the image-to-class measure. This metric is achieved by k-NN search in the local descriptors, in this way, we can make full use of all the local features of a category, thus expressing the distribution of this class more richly and effectively. © 2022 IEEE.

关键词： image classification

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Theoretical mechanism and research progress of thermally activated delayed fluorescence and room temperature phosphorescence molecules

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分子科学学报 2024年第6期40卷 471-487页

作者： ZHAO Xin SONG Yuzhi LIN Lili WANG Chuankui FAN Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean EnergySchool of Physics and ElectronicsShandong Normal UniversityJinan 250014China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 511442China

Thermally activated delayed fluorescence(TADF)and room temperature phosphorescence(RTP)molecules hold promising application prospects in the field of organic light emitting diodes(OLEDs),primarily attributed to their significant advantages in enhancing device stability and lumines-cence ***,TADF and RTP molecules can achieve nearly 100%exciton utilization without necessitating costly and limited precious metal ***,the primary challenges confronting TADF and RTP molecules at present encompass limitations in emission color,low luminescence efficien-cy,severe efficiency roll-off and so *** these points,this paper presents a comprehensive overview of the latest research progress in TADF and RTP *** delve into the mechanisms by which TADF molecules achieve efficient fluorescence emission through unique molecular structural designs,fre-quently involving sophisticated intramolecular charge transfer processes and precise energy level ***,we provide an in-depth analysis of the unique luminescence properties and photo-physical mechanisms of RTP ***,the article focuses on the design strategies for TADF and RTP molecules,encompassing the manipulation of molecular structures,electronic structures and the enhancement of charge transfer *** examining these strategies,we aim to provide a com-prehensive perspective on the research of TADF and RTP *** hope that through this review,it could offer some guidance for future research and inspire the exploration of more innovative TADF and RTP molecules.

关键词： thermally activated delayed fluorescence room temperature phosphorescence excited state property TVCF method QM/MM method

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Scene Text Detection based on Dual-branch Multi-resolution Feature-aware Enhancement Network 10

Scene Text Detection based on Dual-branch Multi-resolution F...

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

作者： Wang, Ruirui Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Rccognition Jiangxi Nanchang China Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Arbitrary shape scene text detection becomes a challenge task due to its background complexity and shape diversity. In this paper, we propose a dual-branch multi-resolution feature-aware enhancement network (DMFE), the lower branch constructs multi-resolution features through a bidirectional feature pyramid network with weights, and the upper branch enhances the perception of multi-scale text at each level through parallel pooling modules with receptive field enhancement. The global and local co-action will integrate high-level semantic information and low-level location information, so as to generate high-quality feature map. Extensive experiments on ICDAR2015, CTW1500 and Total-Text datasets show that the proposed method effectively improves the detection performance of natural scene text. © 2022 IEEE.

关键词： Semantics

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Low-light image enhancement model based on self-calibration illumination and color adjustment 2

Low-light image enhancement model based on self-calibration ...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Zhang, Guiwen Li, Qishen Li, Qiufeng Wu, Taida School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved. © 2022 IEEE.

关键词： Convolution

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Recloss: Random Epipolar Constraint Loss Function for Supervised Optical Flow Estimation Models

SSRN

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SSRN 2023年

作者： Fan, Zhengyuan Cai, Zemin The Department of Electronic Engineering Shantou University Guangdong Shantou515063 China Key Laboratory of Digital Signal and Image Processing of Guangdong Province Guangdong Shantou515063 China

The majority of supervised models estimate optical flow through minimizing the numerical difference between the predicted flow and the ground truth, resulting in the loss of positional and geometric characteristics of the calculated flow fields. In addition, these models require a large number of parameters and high computational cost when computing optical flow. To address these issues, this paper presents a novel loss function and a lightweight framework for optical flow estimation. The proposed loss function, called the random epipolar constraint loss function (RECLoss), incorporates epipolar geometry into supervised optimization to transform the numerical difference into geometry constraint. The RECLoss can make the optical flow estimation models more effective and enhance their generalization abilities. Moreover, the design of RECLoss is more interpretable and the estimated optical flow fields from RECLoss have clearly defined mathematical meanings. A lightweight recurrent neural network for optical flow estimation (LRFlow) that balances computational cost and estimation accuracy is also proposed. The LRFlow, containing only 3.0M parameters, consists of a feature extractor, a correlation matching module, and an iterative update unit. The proposed lightweight network achieves state-of-the-art results compared to all other lightweight networks on the challenging Sintel and KITTI datasets. The effectiveness of RECLoss and LRFlow is demonstrated through extensive experiments. The feasibility of RECLoss has also been verified by appending to two state-ofthe-art supervised optical flow estimation models, RAFT and GMA. The models trained with RECLoss even outperform the same ones with other improvements. Experimental results show that RECLoss does not entail a substantial increase in computational overhead. Specifically, among the 290K pixel points, a mere 0.9K pixel points are utilized for RECLoss calculation. Remarkably, the models trained with RECLoss exhibit super

关键词： Optical flows

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Abundant Semantic Information Module for Object Detection 2

Abundant Semantic Information Module for Object Detection

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Wu, Taida Li, Qishen Li, Qiufeng Gao, Jun School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement. © 2022 IEEE.

关键词： Object detection

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An Improved Embedding Model for Zero-Shot Classification based on Attention Mechanism 5

An Improved Embedding Model for Zero-Shot Classification bas...

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5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Wu, Zongfeng Li, Qishen Yu, Xiao Peng, Xu Nanchang Hangkong University School of Information Engineering Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

It is well-known that the auxiliary information plays a key role in zero-shot classification. However, most of the existing popular methods do not make effective use of auxiliary information. To address this issue, we propose an improved embedding model for zero-shot classification based on attention mechanism, called EMAM. In the proposed EMAM, we first add an attention mechanism to effectively extract the key information of auxiliary information in zero-shot classification. Then optimizes the objective function to improve the recognition rate of this model. Finally the experimental comparison is implemented on the standard zero-shot learning datasets. The experimental results demonstrate that our proposed EMAM not only verifies its validity, but also achieves good results. © 2021 IEEE.

关键词： Zero-shot learning

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Hydrostatic Pressure Responsive Mechanism for Room Temperature Phosphorescence: A Theoretical Perspective

SSRN

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SSRN 2023年

作者： Wang, Yan Han, Shulin Mu, Qingfang Liu, Huanling Chuankui, Wang Fan, Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China

Stimulus-responsive organic room temperature phosphorescence (RTP) materials are a class of materials that show emission property changes (color, efficiency and lifetime) when subjected to diverse environmental stimuli including light, temperature, force, electricity, etc. The change of molecular stacking modes and intermolecular interactions can result in mechanical stimulus-induced emission switching behavior. Most of reported mechanical luminescent molecules show red-shifted emission under the action of mechanical stress such as pressure and grinding, but rarely show blue-shifted emission phenomenon. The underlying mechanism of this abnormal mechanoresponsive luminescent material is still unclear. Herein, based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations, the photophysical properties with response to hydrostatic pressure of RTP molecules in crystal are theoretically studied. The excited state dynamic processes are investigated by using quantum mechanics and molecular mechanics (QM/MM) method coupled with thermal vibration correlation function (TVCF) method. Huang-Rhys factors and reorganization energies are analyzed, and natural atomic orbital is used to calculate the orbital information of the excited states. Furthermore, intermolecular interactions are visualized using independent gradient model based on Hirshfeld partition (IGMH) and quantitatively measured by the molecular force field energy decomposition method. Based on these methods, we focus on the effects of hydrostatic pressure induced molecular conformational transformations, the changes of intermolecular interactions and charge-transfer characteristics on the emission wavelengths, efficiencies and lifetimes under stimulation. Results show that the increase of pressure can blue-shift the RTP emissions which is mainly caused by the change of molecular conformation and the increase of bending vibration frequency. Under 2 Gpa, large spin-orbit coup

关键词： Quantum theory

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Attentive Feature Focusing for Person Search by Natural Language 18th

Attentive Feature Focusing for Person Search by Natural La...

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18th International Forum of Digital Multimedia communication, IFTC 2021

作者： Pan, Renjie Yang, Hua Yang, Xinxin Xiaodong, Mao Ye, Long Pan, Da The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China Science and Technology Department of Shanghai Public Security Bureau Shanghai China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

ISBN: (纸本)9789811922657

Cross-modal Retrieval has made great improvement since social media and artificial intelligence is gradually changing our life style. Human interaction is no longer limited to a single mode. Cross-modal retrieval brings greater portability to users. Extracting more sufficient feature from multimedia data and better fusing the feature have become the focus of academic research. In response to these two issues, this paper applies deep learning as the foundation for person search by natural language, combining CNN and Transformer. Meanwhile, we utilize BERT, the most effective feature extraction network in Natural Language processing and proposed a network architecture using attention mechanism. The framework is divided into a global branch and a fine-grained branch. At the same time, a human parsing network is designed to classify different parts of the characters to obtain feature representation in attribute level. In the visual-textual alignment, the k-reciprocal sampling algorithm is used to construct a more comprehensive sample pair. The proposed framework has achieved state-of-the-art performance in CUHK-PEDES, which proves its great capacity and prospect. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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