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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Understanding News Creation Intents: Frame, Dataset, and Method

arXiv

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arXiv 2023年

作者： Wang, Zhengjia Wang, Danding Sheng, Qiang Cao, Juan Su, Silong Sun, Yifan Hu, Beizhe Ma, Siyuan Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China University of Macau China

As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest. News intent refers to the purpose or intention behind the creation of a news article. While the significance of research on news intent has been widely acknowledged, the absence of a systematic news intent understanding framework hinders further exploration of news intent and its downstream applications. To bridge this gap, we propose News INTent (NINT) frame, the first component-aware formalism for understanding the news creation intent based on research in philosophy, psychology, and cognitive science. Within this frame, we define the news intent identification task and provide a benchmark dataset with fine-grained labels along with an efficient benchmark method. Experiments demonstrate that NINT is beneficial in both the intent identification task and downstream tasks that demand a profound understanding of news. This work marks a foundational step towards a more systematic exploration of news creation intents. Copyright © 2023, The Authors. All rights reserved.

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Automatic Survey Generation Based Onposition Closeness of Key Words

SSRN

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SSRN 2022年

作者： Sun, Xiaoping Zhuge, Hai Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Chinese Academy of Sciences China

Survey enables researchers to know a research area quickly. Automatic survey generation is a challenge to text summarization research because a survey consists of many sections, each of which is composed by the sentences selected from the papers most relevant to its titles. The key is to rank papers, determine top-k papers and select sentences from the top-k papers according to section title. This paper proposes a measure called key word position closeness to rank papers by measuring how closely two neighboring key words of a section title are distributed within a paper. The basic rationale is that the positions of the neighboring key words of a section title should be closer in more relevant papers to be surveyed. To select the top-k papers for each section, an unsupervised method is proposed to predict the top-k value based on the shape of the curve of the sorted ranking scores. Based on the duality property of the closeness, sentence ranking scores of a selected paper with respect to the key words of section title can be directly obtained when the paper ranking score is computed using the closeness, both the importance and coherence of selected sentences can be reflected without extra sentence ranking calculation. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with term frequency-based approaches to generating scientific survey. © 2022, The Authors. All rights reserved.

关键词： Text processing

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Two-particle debris flow simulation based on SPH

Two-particle debris flow simulation based on SPH

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作者： Zhang, Jiaxiu Yang, Meng Li, Xiaomin Jiang, Qun'ou Zhang, Heng Meng, Weiliang School of Information Science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Soil and Water Conservation Beijing Forestry University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Debris flow is a highly destructive natural disaster, necessitating accurate simulation and prediction. Existing simulation methods tend to be overly simplified, neglecting the three-dimensional complexity and multiphase fluid interactions, and they also lack comprehensive consideration of soil conditions. We propose a novel two-particle debris flow simulation method based on smoothed particle hydrodynamics (SPH) for enhanced accuracy. Our method employs a sophisticated two-particle model coupling debris flow dynamics with SPH to simulate fluid-solid interaction effectively, which considers various soil factors, dividing terrain into variable and fixed areas, incorporating soil impact factors for realistic simulation. By dynamically updating positions and reconstructing surfaces, and employing GPU and hash lookup acceleration methods, we achieve accurate simulation with significantly efficiency. Experimental results validate the effectiveness of our method across different conditions, making it valuable for debris flow risk assessment in natural disaster management. © 2024 John Wiley & Sons Ltd.

关键词： Risk assessment

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Hand Detection Based on YOLOv3 6th

Hand Detection Based on YOLOv3

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Yu, Xipu Ma, Linjuan Zhang, Fuquan International Curriculum Center The High School Affiliated To Renmin University of China Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Hand detection is paid more attention to in the field of augmented reality, which can enhance user experience of human–computer interaction. It is more and more popular to use deep neural networks to research hand detection. A new method of hand detection based on YOLOv3 Model is proposed in our work. Our target is to contribute to a real-time hand detection system to make it more accuracy and convenient for the experience of the user and can apply in all aspects of our life. 11 K hands dataset is used to perfrom the experiments and the result is sound. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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mmFAS: Multimodal Face Anti-Spoofing Using Multi-Level Alignment and Switch-Attention Fusion 39

mmFAS: Multimodal Face Anti-Spoofing Using Multi-Level Align...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Geng Xie, Wuyuan Lin, Di Liu, Ye Wang, Miaohui College of CSSE Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China College of Intelligence and Computing Tianjin University China School of Automation Nanjing University of Posts and Telecommunications China

ISBN: (纸本)157735897X

The increasing number of presentation attacks on reliable face matching has raised concerns and garnered attention towards face anti-spoofing (FAS). However, existing methods for FAS modeling commonly fuse multiple visual modalities (e.g., RGB, Depth, and Infrared) in a straightforward manner, disregarding latent feature gaps that can hinder representation learning. To address this challenge, we propose a novel multimodal FAS framework (mmFAS) that focuses on explicit alignment and fusion of latent features across different modalities. Specifically, we develop a multimodal alignment module to alleviate the latent feature gap by using instance-level contrastive learning and class-level matching simultaneously. Further, we explore a new switch-attention based fusion module to automatically aggregate complementary information and control model complexity. To evaluate the anti-spoofing performance more effectively, we adopt a challenging yet meaningful cross-database protocol involving four benchmark multimodal FAS datasets to simulate real-world scenarios. Extensive experimental results demonstrate the effectiveness of mmFAS in improving the accuracy of FAS systems, outperforming 10 representative methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Redefinable neural network for structured light array

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Advanced Photonics Nexus 2024年第5期3卷 151-161页

作者： Hengyang Li Jiaming Xu Huaizhi Zhang Cong Hu Zining Wan Yu Xiao Xiahui Tang Chenhao Wan Gang Xu Yingxiong Qin Huazhong University of Science and Technology School of Optical and Electronic Information and Wuhan National Laboratory for OptoelectronicsNational Engineering Research Center for Laser ProcessingOptics Valley LaboratoryWuhanChina Communication University of China Neuroscience and Intelligent Media InstituteBeijingChina

Neural networks have provided faster and more straightforward solutions for laser ***,their effectiveness when facing diverse structured lights and various output resolutions remains vulnerable because of the specialized end-to-end training and static ***,we propose a redefinable neural network(RediNet),realizing customized modulation on diverse structured light arrays through a single general *** network input format features a redefinable dimension designation,which ensures RediNet wide applicability and removes the burden of processing pixel-wise light *** prowess of originally generating arbitrary-resolution holograms with a fixed network is first *** versatility is showcased in the generation of 2D/3D foci arrays,Bessel and Airy beam arrays,(perfect)vortex beam arrays,and even snowflake-intensity arrays with arbitrarily built phase functions.A standout application is producing multichannel compound vortex beams,where RediNet empowers a spatial light modulator(SLM)to offer comprehensive multiplexing functionalities for free-space optical ***,RediNet has the hitherto highest efficiency,only consuming 12 ms(faster than the mainstream SLM framerate of 60 Hz)for a 1000^(2)-resolution holograph,which is critical in real-time required *** the fine resolution,high speed,and unprecedented universality,RediNet can serve extensive applications,such as next-generation optical communication,parallel laser direct writing,and optical traps.

关键词： structured light neural network computer-generated holograph beam array

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual information

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jicun Li Xingjian Li Tianyang Wang Shi Wang Yanan Cao Chengzhong Xu Dejing Dou Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Big Data Lab Baidu Research State Key Lab of IOTSC University of Macau University of Alabama at Birmingham Institute of Information Engineering Chinese Academy of Sciences

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method.

关键词： Cross layer design Bit error rate Signal processing Stability analysis Robustness Acoustics Behavioral sciences

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Fixation guided network for salient object detection 2

Fixation guided network for salient object detection

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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Cui, Zhe Su, Li Zhang, Weigang Huang, Qingming University of Chinese Academy of Sciences China Harbin Institute of Technology Weihai China University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450383080

Convolutional neural network (CNN) based salient object detection (SOD) has achieved great development in recent years. However, in some challenging cases, i.e. small-scale salient object, low contrast salient object and cluttered background, existing salient object detect methods are still not satisfying. In order to accurately detect salient objects, SOD networks need to fix the position of most salient part. Fixation prediction (FP) focuses on the most visual attractive regions, so we think it could assist in locating salient objects. As far as we know, there are few methods jointly consider SOD and FP tasks. In this paper, we propose a fixation guided salient object detection network (FGNet) to leverage the correlation between SOD and FP. FGNet consists of two branches to deal with fixation prediction and salient object detection respectively. Further, an effective feature cooperation module (FCM) is proposed to fuse complementary information between the two branches. Extensive experiments on four popular datasets and comparisons with twelve state-of-the-art methods show that the proposed FGNet well captures the main context of images and locates salient objects more accurately. © 2021 ACM.

关键词： Computer vision

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