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12th CCF Conference on BigData, BigData 2024

作者： Zhang, Yiming Ao, Xiang Yu, Guoxin He, Qing Henan Institute of Advanced Technology Zhengzhou University Zhengzhou450002 China Beijing100190 China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China

ISBN: (纸本)9789819610235

Event-level Financial Sentiment Analysis (EFSA) aims to extract all the quintuples containing five sentiment elements from a given financial news text, which has gained prominence as an emerging domain recently. The present study utilizes a 4-hop Chain-of-Thought (CoT) prompting based on LLMs to predict sentiment elements in a fixed order, which neglects the interdependencies among the sentiment elements within a quintuple. Inspired by recent multi-view prompting (MvP) and CoT ideas, we propose a novel framework termed Retrieval-Augmented Multipath Chain-of-Thought (RMP-CoT) that aggregates quintuples generated by LLMs through different reasoning paths, leveraging a retrieval-augmented mechanism. Specifically, RMP-CoT integrates different element orders into CoT prompting to guide LLMs in generating multiple sentiment quintuples through the utilization of retrieval-augmented mechanism, and then selects the most plausible quintuples by voting. To investigate the effectiveness of our framework, we conduct extensive experiments on four benchmark tasks of EFSA. RMP-CoT pushes the state-of-the-art by over 6% F1 on the EFSA task and also performs quite effectively on the other sub-tasks of EFSA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chain-of-Thought Event-level Sentiment Analysis Financial Sentiment Analysis

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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Teaching Chinese sign language with a smartphone

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Virtual Reality & intelligent Hardware 2021年第3期3卷 248-260页

作者： Yanxiao ZHANG Yuecong MIN Xilin CHEN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Background There is a large group of deaf-mutes worldwide, and sign language is a major communication tool in this community. It is necessary for deaf-mutes to be able to communicate with others who are capable of hearing, and hearing people also need to understand sign language, which produces a great demand for sign language tuition. Even though there have already been a large number of books written about sign language, it is inefficient to learn sign language through reading alone, and the same can be said on watching videos. To solve this problem, we developed a smartphone-based interactive Chinese sign language teaching system that facilitates sign language learning. Methods The system provides a learner with some learning modes and captures the learner's actions using the front camera of the smartphone. At present, the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary, and the system will return the top three retrieved candidates;thus, it can remind the learners what the sign is. Results This system provides interactive learning to enable a user to efficiently learn sign language. The system adopts an algorithm based on point cloud recognition to evaluate a user's sign and costs about 700ms of inference time for each sample, which meets the real-time requirements. Conclusion This interactive learning system decreases the communication barriers between deaf-mutes and hearing people.

关键词： Computer assisted learning Sign language evaluation Smartphone platform

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Data-Free Network Debiasing for Long-Tailed Visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Food computing for Nutrition and Health

Food Computing for Nutrition and Health

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International Conference on Data Engineering Workshops

作者： Shuqiang Jiang Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

Dietary intake is the main source of human nutrition and thus an important material foundation for maintaining human health. Food computing applies computational approaches to intelligently obtain dietary categories, ingredients, and nutrient intake from dietary data, and then provides dietary recommendations to users based on various dietary factors. Therefore, it can achieve intelligent dietary management to assist in solving human health issues. For its significant impact on human health, it has received more attention from both academia and industry, and has made rapid development, especially with the booming Artificial Intelligence (AI). This talk will first introduce the definition, methods, tasks and applications of food computing. Then we will discuss its various tasks, ranging from vision-based multi-granularity food analysis, multimodal recipe analysis, food knowledge graph construction and dietary recommendation. Finally, we will point out future research directions on food computing, such as large food language/multimodal models and the emerging interdisciplinary field AI4Food from the synergy between AI and food science. We expect that this talk will provide the researchers with a more comprehensive understanding of the field, and provide perspectives for the next wave of development in this rapidly growing field.

关键词： Industries Conferences Computational modeling Knowledge graphs Data engineering Distance measurement Artificial intelligence

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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations

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Patterns 2025年第5期6卷

作者： Zhou, Pengfei Min, Weiqing Fu, Chaoran Jin, Ying Huang, Mingyu Li, Xiangyang Mei, Shuhuan Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou China Zhongke Shenjian Technology Co. Ltd. Suzhou China

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky significantly outperforms general-purpose LLMs on the Chinese National Chef Examination and Dietetic Examination, achieving an accuracy of 83.3% and 91.2%, respectively. Beyond enhancing culinary creativity and promoting healthier eating patterns, FoodSky aims to establish a new benchmark for domain-specific LLMs in addressing real-world food-related challenges. © 2025 The Author(s)

关键词： Food ingredients

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AlphaFold2 and its applications in the fields of biology and medicine

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Signal Transduction and Targeted Therapy 2023年第4期8卷 1630-1643页

作者： Zhenyu Yang Xiaoxi Zeng Yi Zhao Runsheng Chen West China Biomedical Big Data Center West China HospitalSichuan UniversityChengdu 610041China Key Laboratory of Intelligent Information Processing Advanced Computer Research CenterInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Key Laboratory of RNA Biology Center for Big Data Research in HealthInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Pingshan Translational Medicine Center Shenzhen Bay LaboratoryShenzhen 518118China

AlphaFold2(AF2)is an artificial intelligence(AI)system developed by DeepMind that can predict three-dimensional(3D)structures of proteins from amino acid sequences with atomic-level *** structure prediction is one of the most challenging problems in computational biology and chemistry,and has puzzled scientists for 50 *** advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much *** release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community,especially in the fields of biology and ***2 is thought to have a significant impact on structural biology and research areas that need protein structure information,such as drug discovery,protein design,prediction of protein function,et *** the time is not long since AF2 was developed,there are already quite a few application studies of AF2 in the fields of biology and medicine,with many of them having preliminarily proved the potential of *** better understand AF2 and promote its applications,we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success,and particularly focus on reviewing its applications in the fields of biology and *** of current AF2 prediction will also be discussed.

关键词： system fields Fold

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Extracting Variable-Depth Logical Document Hierarchy from Long Documents:Method,Evaluation,and Application

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Journal of Computer Science & technology 2022年第3期37卷 699-718页

作者： Rong-Yu Cao Yi-Xuan Cao Gan-Bin Zhou Ping Luo Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China WeChat Search Application Department Tencent Holdings Ltd.Beijing 100080China Peng Cheng Laboratory Shenzhen 518066China

In this paper,we study the problem of extracting variable-depth"logical document hierarchy"from long documents,namely organizing the recognized"physical document objects"into hierarchical *** discovery of logical document hierarchy is the vital step to support many downstream applications(e.g.,passage-based retrieval and high-quality information extraction).However,long documents,containing hundreds or even thousands of pages and a variable-depth hierarchy,challenge the existing *** address these challenges,we develop a framework,namely Hierarchy Extraction from Long Document(HELD),where we"sequentially"insert each physical object at the proper position on the current *** whether each possible position is proper or not can be formulated as a binary classification *** further improve its effectiveness and efficiency,we study the design variants in HELD,including traversal orders of the insertion positions,heading extraction explicitly or implicitly,tolerance to insertion errors in predecessor steps,and so *** for evaluations,we find that previous studies ignore the error that the depth of a node is correct while its path to the root is *** such mistakes may worsen the downstream applications seriously,a new measure is developed for a more careful *** empirical experiments based on thousands of long documents from Chinese financial market,English financial market and English scientific publication show that the HELD model with the"root-to-leaf"traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972,6,0.729,1 and 0.957,8 in the Chinese financial,English financial and arXiv datasets,***,we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval *** summary,we conduct a systematic study on this task in terms of methods

关键词： logical document hierarchy long documents passage retrieval

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