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arXiv 2023年

作者： Ye, Ziyang Yu, Haiyang Li, Bin Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Heatmap-based methods play an important role in anatomical landmark detection. However, most current heatmap-based methods assume that the distributions of all landmarks are the same and the distribution of each landmark is isotropic, which may not be in line with reality. For example, the landmark on the jaw is more likely to be located along the edge and less likely to be located inside or outside the jaw. Manually annotating tends to follow similar rules, resulting in an anisotropic distribution for annotated landmarks, which represents the uncertainty in the annotation. To estimate the uncertainty, we propose a module named Pyramid Covariance Predictor to predict the covariance matrices of the target Gaussian distributions, which determine the distributions of landmarks and represent the uncertainty of landmark annotation. Specifically, the Pyramid Covariance Predictor utilizes the pyramid features extracted by the encoder of the backbone U-Net and predicts the Cholesky decomposition of the covariance matrix of the landmark location distribution. Experimental results show that the proposed Pyramid Covariance Predictor can accurately predict the distributions and improve the performance of anatomical landmark detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural information processing Systems, NeurIPS 2024

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

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. © 2024 Neural information processing systems foundation. All rights reserved.

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FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks 25

FediLive: A Framework for Collecting and Preprocessing Snaps...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shaojie Min Shaobin Wang Yaxiao Luo Min Gao Qingyuan Gong Yu Xiao Yang Chen Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Department of Information and Communications Engineering Aalto University Espoo Finland

ISBN: (纸本)9798400713316

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.

关键词： decentralized online social networks

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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Fuzzy Concession Strategy for Emotional Human-computer Negotiation 36

Fuzzy Concession Strategy for Emotional Human-Computer Negot...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Luo, Xudong Luo, Ying Sun, Kaili Li, Yanling School of Computer Science and Engineering Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China

ISBN: (纸本)9798331527235

This paper proposes an emotion-based human-computer negotiation system. Existing systems handle basic dialogues but struggle with complex negotiations involving human emotions. To address this, we design a fuzzy concession strategy that adjusts tactics through sentiment analysis, managing states like anger, happiness, and anxiety. Experiments show significant improvement in negotiation success and overall gains when considering emotional factors. Results demonstrate that this model enhances customer satisfaction and user experience by achieving agreements that satisfy users. This research highlights the potential of integrating fuzzy reasoning, sentiment analysis, and natural language processing into AI systems, laying the foundation for more intelligent, human-like dialogue agents and improving human-computer interaction and e-commerce engagement. © 2024 IEEE.

关键词： Natural language processing systems

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A Legal Multi-Choice Question Answering Model Based on DeBERTa and Attention Mechanism 36

A Legal Multi-Choice Question Answering Model Based on DeBER...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Luo, Ying Luo, Xudong Chen, Guibin School of Computer Science and Engineering Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guilin541004 China

ISBN: (纸本)9798331527235

This paper presents a novel multi-choice question answering model tailored for the legal domain, which integrates a DeBERTa-based framework with bilinear attention mechanisms. The model is designed to enhance the interpretation of complex legal texts, utilising vector similarity searches to align questions with pertinent statutes and a neural network to analyse this data effectively. It aims to improve the accuracy of legal question answering by employing advanced techniques of natural language processing. A binary classifier is also incorporated to assess answer options and generate conclusive responses. Experimental results demonstrate the model's superiority over traditional methods, particularly in handling intricate legal questions. This study underscores the model's potential to advance legal AI technology significantly, offering substantial benefits for judicial examinations and legal professionals. © 2024 IEEE.

关键词： Bilinear attention mechanism Legal article matching Legal question answering Natural language processing Neural network

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TaMMa: Target-driven Multi-subscene Mobile Manipulation 8

TaMMa: Target-driven Multi-subscene Mobile Manipulation

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8th Conference on Robot Learning, CoRL 2024

作者： Hou, Jiawei Wang, Tianyu Pan, Tongying Wang, Shouyan Xue, Xiangyang Fu, Yanwei School of Computer Science Fudan University China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Shanghai Key Lab of Intelligent Information Processing Technology Innovation Center of Calligraphy and Painting Digital Generation Ministry of Culture and Tourism China

For everyday service robotics, the ability to navigate back and forth based on tasks in multi-subscene environments and perform delicate manipulations is crucial and highly practical. While existing robotics primarily focus on complex tasks within a single scene or simple tasks across scalable scenes individually, robots consisting of a mobile base with a robotic arm face the challenge of efficiently representing multiple subscenes, coordinating the collaboration between the mobile base and the robotic arm, and managing delicate tasks in scalable environments. To address this issue, we propose Target-driven Multi-subscene Mobile Manipulation (TaMMa), which efficiently handles mobile base movement and fine-grained manipulation across subscenes. Specifically, we obtain a reliable 3D Gaussian initialization of the whole scene using a sparse 3D point cloud with encoded semantics. Through querying the coarse Gaussians, we acquire the approximate pose of the target, navigate the mobile base to approach it, and reduce the scope of precise target pose estimation to the corresponding subscene. Optimizing while moving, we employ diffusion-based depth completion to optimize fine-grained Gaussians and estimate the target's refined pose. For target-driven manipulation, we adopt Gaussians inpainting to obtain precise poses for the origin and destination of the operation in an imagine before you do it manner, enabling fine-grained manipulation. We conduct various experiments on a real robotic to demonstrate our method in effectively and efficiently achieving precise operation tasks across multiple tabletop subscenes. © 2024 Proceedings of Machine Learning Research.

关键词： Gaussian distribution

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Soft-labeled Contrastive Pre-training for Function-level Code Representation

Soft-Labeled Contrastive Pre-training for Function-level Cod...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Xiaonan Guo, Daya Gong, Yeyun Lin, Yun Shen, Yelong Qiu, Xipeng Jiang, Daxin Chen, Weizhu Duan, Nan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Computer Science Fudan University China Microsoft National University of Singapore Singapore

Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present SCodeR, a Soft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level Code Representation. Considering the relevance between codes in a large-scale code corpus, the soft-labeled contrastive pre-training can obtain fine-grained soft-labels through an iterative adversarial manner and use them to learn better code representation. The positive sample construction is another key for contrastive pre-training. Previous works use transformation-based methods like variable renaming to generate semantically equal positive codes. However, they usually result in the generated code with a highly similar surface form, and thus mislead the model to focus on superficial code structure instead of code semantics. To encourage SCodeR to capture semantic information from the code, we utilize code comments and abstract syntax sub-trees of the code to build positive samples. We conduct experiments on four code-related tasks over seven datasets. Extensive experimental results show that SCodeR achieves new state-of-the-art performance on all of them, which illustrates the effectiveness of the proposed pretraining method. © 2022 Association for Computational Linguistics.

关键词： Iterative methods

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Trusted data access and authorization protocol

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The Journal of China Universities of Posts and Telecommunications 2021年第5期28卷 18-26页

作者： Song Wenpeng Li Ming Liu Baixiang Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China School of Computer Science Fudan UniversityShanghai 200433China China Electronics Standardization Institute Beijing 100007China

Threshold proxy re-encryption(PRE) authorizes the data access right of data subject to multiple proxies, who authorize the right again to delegatee to accomplish the end-to-end data encryption process from storage to authorization. Based on threshold PRE algorithm, in order to build a complete trusted data storage and authorization system, the four protocols, which are data access protocol, authorization proxy protocol, authorization proxy cancellation protocol and data reading authorization protocol, are defined completely. On that basis, an efficient data searching method is constructed by specifying the data delegatee. At last, to ensure the right to know of data, the audit log is processed with trusted data right confirmation based on distributed ledger technology. Meanwhile, a parallel data right confirmation processing method is defined based on hierarchical derivation algorithm of public and private key. In the end, the performance evaluation analysis of the protocol are given. Trusted data access and authorization protocol is convenient to build a complete data processing system on the premise of protecting data privacy based on public cloud storage system or distributed storage system.

关键词： proxy re-encryption(PRE) data privacy distributed ledger technology blockchain

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Retrieval Augmented Recipe Generation

arXiv

引用

arXiv 2024年

作者： Liu, Guoshan Yin, Hailong Zhu, Bin Chen, Jingjing Ngo, Chong-Wah Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore Management University Singapore

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallucinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset. © 2024, CC BY-NC-ND.

关键词： Food ingredients

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