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SSRN 2024年

作者： Tian, Mingkai Li, Guorong Qi, Yuankai Wang, Shuhui Sheng, Quan Z. Huang, Qingming School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computing Macquarie University Australia Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences China

Video captioning, a challenging task targeting the automatic generation of accurate and comprehensive descriptions based on video content, has witnessed substantial success recently driven by bridging video representations and textual semantics. Inspired by the nature of the video retrieval task, which learns visual features strongly related to text queries, we propose to take advantage of visual representation learning from the video retrieval framework to tackle video captioning tasks. However, a simple direct application of video retrieval models performs poorly due to the weak ability to capture sufficient video details and temporal information required for video captioning. To increase the attention on details, we propose a learnable token shift module, which flexibly captures subtle movements in local regions across the temporal sequence. Furthermore, we devise a Refineformer, which learns to integrate local video tokens strongly related to desired captions via a cross-attention mechanism. Extensive experiments on MSVD, MSR-VTT and VATEX demonstrate the favorable performance of our method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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MambaLCT: Boosting Tracking via Long-term Context State Space Model

arXiv

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arXiv 2024年

作者： Li, Xiaohai Zhong, Bineng Liang, Qihua Li, Guorong Mo, Zhiyi Song, Shuxiang Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Colleges Universities Key Laboratory of Intelligent Software Wuzhou University Wuzhou543002 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100101 China

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model’s ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds. Code and models are available at https://***/GXNU-ZhongLab/MambaLCT. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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When liver disease diagnosis encounters deep learning:Analysis,challenges,and prospects

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iLIVER 2023年第1期2卷 73-87页

作者： Yingjie Tian Minghao Liu Yu Sun Saiji Fu School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of SciencesBeijing 100190China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190China Tsinghua University Press Beijing 102611China School of Economics and Management Beijing University of Posts and TelecommunicationsBeijing 100876China

The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic ***,obesity,alcohol use,and other factors can damage the liver and cause liver *** diagnosis of liver disease used to depend on the clinical experience of doctors,which made it subjective,difficult,and *** learning has made breakthroughs in various fields;thus,there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and *** this paper,we provide an overview of deep learning in liver research using 139 papers from the last 5 *** also show the relationship between data modalities,liver topics,and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic,in addition to the relations and trends between these ***,we discuss the challenges of and expectations for deep learning in liver research.

关键词： Liver Deep learning data modality Liver topic Application

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Iterative Privileged Learning for Multi-view Classification 10

Iterative Privileged Learning for Multi-view Classification

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10th International Conference on Information Technology and Quantitative management, ITQM 2023

作者： Fu, Saiji Tian, Yingjie Tang, Jingjing School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Sichuan 611130 China

Multi-view data is closely associated with privileged information, presenting opportunities for advancements in current studies. Firstly, they often overlook the dynamic nature of privileged information;Secondly, the current state-of-the-art privileged support vector machine, dSVM+, is primarily designed for single-view learning;Thirdly, some methods only address two-view classification problems;Finally, the performance enhancements achieved by these approaches remain unsatisfactory. To this end, this paper proposes a multi-view privileged support vector machine called MVdSVM+. The iterative learning strategy is put forward to update the privileged information dynamically. To our best knowledge, this is the first attempt of dSVM+ in multi-view learning. Meanwhile, it is designed for multiple views. Experiments show the effectiveness of MVdSVM+ over the benchmark methods. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative management.

关键词： Classification (of information)

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg.

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

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Not all diffusion model activations have been evaluated as discriminative features 24

Not all diffusion model activations have been evaluated as d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

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Types and Evolutionary Boundaries of Agents 5

Types and Evolutionary Boundaries of Agents

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liu, Feng Liu, Ying Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Economics and Management Beijing China

ISBN: (纸本)9781665426305

Through the analysis of the characteristics of natural intelligent systems, the three laws of intelligence are exploringly proposed. The most important point is that any agent is a system with knowledge input, output, (dynamic) storage and creation abilities Since an agent has four abilities, and each ability has three states, including zero, infinity, and finite (between zero and infinity), 81 types of agents can be formed. Therefore, it can be inferred that Alpha point (No. 1), where all the four abilities of an agent are zero, and Omega point (No. 81), where all four abilities are infinite are the boundary of the evolution of Agent. This paper designs experiments to detect these 81 agents, and finds that 11 of them can be confirmed by experiments at present. Through the discussion of the states of Agents No. 64 to No. 81, fresh thinking is proposed for the generation and realization of the paradox. © 2021 IEEE.

关键词： Evolution of Agents Types of Agents α Point Ω Point

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Credit and guarantee insurance: A consistent pricing approach in supply chain finance 9th

Credit and guarantee insurance: A consistent pricing approac...

引用

9th International Conference on Information Technology and Quantitative management, ITQM 2022

作者： Zhang, Linzi Xue, Jiayu Shang, Mengyu Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Credit insurance is a new loan guarantee contract which can effectively stimulate domestic demand and promote consumption. According to different modes of payment, individual credit loan guarantees mainly include credit insurance and guarantee insurance. In this paper, we apply the Complete Information Game to analyze the strategies of the insured subject and the risk subject under different scenarios of personal loan guarantee, and obtain the influencing factors of each decision-making subject in supply chain finance. The conclusion shows that the credit status of individual loan applicants greatly affects the decision-making of supply chain financial entities such as banks. Based on this, this paper puts forward corresponding suggestions for the credit insurance business management. © 2022 The Authors. Published by Elsevier B.V.

关键词： Costs

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Pyramid scheme in stock market:A kind of financial market simulation

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Chinese Physics B 2021年第9期30卷 624-632页

作者： Yong Shi Bo Li Guang-Le Du School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China

Artificial stock market simulation based on agent is an important means to study financial *** on the assumption that the investors are composed of a main fund,small trend and contrarian investors characterized by four parameters,we simulate and research a kind of financial phenomenon with the characteristics of pyramid *** simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors,the small investors'parameters of taking profit and stopping loss,the order size of the main fund and the strategies adopted by the main *** work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets,design trading rules for regulators and develop trading strategies for investors.

关键词： pyramid scheme agent-based model artificial stock market herd behavior

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