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12th International Conference on Learning Representations, ICLR 2024

作者： Xiao, Jiayu Lv, Henglei Li, Liang Wang, Shuhui Huang, Qingming Key Lab of Intelligent Information Processing ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey appropriate spatial composition specified by a layout instruction. In this work, we probe into zero-shot grounded T2I generation with diffusion models, that is, generating images corresponding to the input layout information without training auxiliary modules or finetuning diffusion models. We propose a Region and Boundary (R&B) aware cross-attention guidance approach that gradually modulates the attention maps of diffusion model during generative process, and assists the model to synthesize images (1) with high fidelity, (2) highly compatible with textual input, and (3) interpreting layout instructions accurately. Specifically, we leverage the discrete sampling to bridge the gap between consecutive attention maps and discrete layout constraints, and design a region-aware loss to refine the generative layout during diffusion process. We further propose a boundary-aware loss to strengthen object discriminability within the corresponding regions. Experimental results show that our method outperforms existing state-of-the-art zero-shot grounded T2I generation methods by a large margin both qualitatively and quantitatively on several benchmarks. Project page: https://***/Region-and-Boundary. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Bridges

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields 6

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Fan, Yujie Luo, Huan Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes. © 2024 IEEE.

关键词： Semantic Segmentation

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Joint Extraction of Interaction Features and Attention Mechanism Fused with Entity-Relations 36

Joint Extraction of Interaction Features and Attention Mecha...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Su, Lisha Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

ISBN: (纸本)1891706594

Joint extraction of entities and relations is an essential task in information extraction. Recently, tagging-based models have gained attention but with poor performance on overlapping triplets, which confronted the issues like cascading errors, information redundancy, and sensitivity to subject extraction. To solve the issue of overlapping triplets, we propose a model combined Gated Attention Unit (GAU) and Multi-head Attention Mechanism (MHA) in this paper. This model distinguishes features for entities and relations, and performs entity recognition for each head entity, different head entities are matched to the same or different tail entities. Then specific tail entity tagging is applied to determine all possible relations and corresponding tails. At the same time, in order to better solve the long-distance dependence and complex multiple relations between entities, GAU and MHA are used to enhance the semantic representation, effectively identify entities and relations, and enhance the learning ability and generalization ability of the model. Experiment results on the public datasets DuIE2.0 and CMeIE show that our model performs better than baselines. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Extending generalized unsupervised manifold alignment

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Science China(information Sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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Depth and Width Adaption of DNN for Data Stream Classification with Concept Drifts 24th

Depth and Width Adaption of DNN for Data Stream Classificati...

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24th International Conference on intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Zhou, XingZhi Liu, Xiang Wen, YiMin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031482311

To handle data stream classification with concept drifts, recent studies have shown that a continuously evolving network structure can achieve better performance. However, firstly, they only change one hidden node at a time, which is not enough to alleviate underfitting or overfitting of network model, leading to model’s inability to fit data well. Secondly, during the growth process of the network, they did not consider reducing hidden layers, which would affect the learning ability of deep neural network models. To overcome these shortcomings, an adaptive neural network structure (ANSN) is proposed to handle data stream classification with concept drifts. ANSN has a completely open structure, its network structure, depth, and width can evolve automatically in online mode. The experimental results on ten popular data stream datasets show that the proposed ANSN outperforms the comparison methods. The codes of the proposed algorithm is available on https://***/ymw12345/***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep neural networks

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Transmittance Spectrum Asymmetry by Photonic Crystals with Vertical Symmetry Breaking 16

Transmittance Spectrum Asymmetry by Photonic Crystals with V...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Zhu, Zhaoxiang Li, Jiaqi Gu, Xin Liang, Zhouxin Wang, Bo Lin, Yuhang Chen, Yujie State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China

ISBN: (纸本)9798350372076

关键词： Photonic crystals

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Auction-based profit maximization offloading in mobile edge computing

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Digital Communications and Networks 2023年第2期9卷 545-556页

作者： Ruyan Wang Chunyan Zang Peng He Yaping Cui Dapeng Wu School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China

Offloading Mobile Devices(MDs)computation tasks to Edge Nodes(ENs)is a promising solution to overcome computation and energy resources limitations of ***,there exists an unreasonable profit allocation problem between MDs and ENs caused by the excessive concern on MD *** this paper,we propose an auction-based computation offloading algorithm,inspiring ENs to provide high-quality service by maximizing the profit of ***,a novel cooperation auction framework is designed to avoid overall profit damage of ENs,which is derived from the high computation delay at the overloaded ***,the bidding willingness of each MD in every round of auction is determined to ensure MD ***,we put forward a payment rule for the pre-selected winner to effectively guarantee auction ***,the auction-based profit maximization offloading algorithm is proposed,and the MD is allowed to occupy the computation and spectrum resources of the EN for offloading if it wins the *** results verify the performance of the proposed *** with the VA algorithm,the ENs profit is increased by 23.8%,and the task discard ratio is decreased by 7.5%.

关键词： Mobile edge computing Computation offloading Heterogeneous network Auction pricing

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Linear Precoding for Faster-Than-Nyquist Signaling with Imperfect Channel State information 16

Linear Precoding for Faster-Than-Nyquist Signaling with Impe...

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16th International Conference on Wireless Communications and Signal processing, WCSP 2024

作者： Wang, Yan Li, Qiang Li, Yingsong Li, Liping Li, Xingwang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Physics and Electronic Information Engineering Henan Polytechnic University China

ISBN: (纸本)9798350390643

In faster-than-Nyquist (FTN) transmission over frequency-selective fading channels, it is essential to eliminate both FTN-induced intersymbol-interference (ISI) (FTN-ISI) and channel-induced ISI (channel-ISI). Although frequency domain equalization (FDE) techniques can mitigate channel-ISI at the receiver, their performance is often inadequate. In contrast, cyclic prefix/suffix (CPS)-G-to-minus-half (GTMH) precoding shows superior effectiveness in mitigating channel-ISI. However, existing CPS-GTMH precoding techniques assume the transmitter obtains perfect channel state information (CSI) estimated at the receiver. This is impractical due to the high latency in satellite communication links. Therefore, this paper explores precoding for FTN signaling under imperfect CSI (ICSI) and proposes CPS-minimum mean squared error (MMSE) and linear pre-equalization (LPE)-MMSE precoding schemes based on the MMSE criterion. Both schemes employ a cyclic prefix to transform the channel-ISI matrix into the circular matrix, thereby avoiding inter-block interference. The primary distinction is that CPS-MMSE precoding handles FTN-ISI and channel-ISI together using MMSE precoding. In contrast, LPE-MMSE precoding addresses FTN-ISI with LPE and channel-ISI with MMSE precoding separately. Simulation results indicate that CPS-MMSE and LPE-MMSE precodings outperform CPS-FDE and LPE-FDE in mitigating channel-ISI when the estimation error is 0.005 and 0.01. For instance, with a packing ratio of 0.9, a roll-off factor of 0.25, and an estimation error of 0.01, CPS-MMSE and LPE-MMSE precodings achieve bit error rate (BER) gains of approximately 0.8 dB and 1 dB at a BER of 1e4, respectively. However, when the estimation error increases to 0.1, the BER performance of CPS-FDE and LPE-FDE surpasses that of the CPS-MMSE and LPE-MMSE precodings, indicating the proposed precoding schemes' unsuitability for larger channel estimation errors. © 2024 IEEE.

关键词： Bit error rate

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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