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arXiv 2024年

作者： Zhao, Minyi Wang, Jie Li, Zhaoyang Zhang, Jiyuan Sun, Zhenbang Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China ByteDance China

Recent studies have shown that Vision Language Large Models (VLLMs) may output content not relevant to the input images. This problem, called the hallucination phenomenon, undoubtedly degrades VLLM performance. Therefore, various anti-hallucination techniques have been proposed to make model output more reasonable and accurate. Despite their successes, from extensive tests we found that augmenting the prompt (e.g. word appending, rewriting, and spell error etc.) may change model output and make the output hallucinate again. To cure this drawback, we propose a new instruct-tuning framework called Prompt Augmentation and Caption Utilization (PACU) to boost VLLM's generation ability under the augmented prompt scenario. Concretely, on the one hand, PACU exploits existing LLMs to augment and evaluate diverse prompts automatically. The resulting high-quality prompts are utilized to enhance VLLM's ability to process different prompts. On the other hand, PACU exploits image captions to jointly work with image features as well as the prompts for response generation. When the visual feature is inaccurate, LLM can capture useful information from the image captions for response generation. Extensive experiments on hallucination evaluation and prompt-augmented datasets demonstrate that our PACU method can work well with existing schemes to effectively boost VLLM model performance. Code is available in https://***/zhaominyiz/PACU. © 2024, CC BY-NC-SA.

关键词： Visual languages

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A Copyright-Preserving and Fair Image Trading SchemeBased on Blockchain

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Tsinghua science and technology 2023年第5期28卷 849-861页

作者： Feng Yu Jiahui Peng Xianxian Li Chunpei Li Bin Qu School of Computer Science&Engineering Guangxi Normal UniversityGuilin 541004Chinaand with the Key Lab of Education Blockchain and Intelligent TechnologyMinistry of EducationGuangxi Normal UniversityGuilin 541004Chinaand also with Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent ProcessingGuangxi Normal UniversityGuilin 541004China Guangxi Power Grid Materials Co Ltd Nanning 530022China

With the proliferation of the Internet,particularly the rise of social media,digital images have gradually become an important part of life,and trading platforms have emerged for buying and selling ***,traditional image trading service providers may disclose users’private information for ***,many image trading platforms disregard the fairness of a transaction and the issue of copyright protection after an image is *** neglect harms the interests of users and affects their enthusiasm for trading.A secure way to safely transact images is *** proposed a copyright-preserving and fair image trading scheme based on blockchain,which combines amplifying locality-sensitive hashing with searchable symmetric encryption to achieve safe image retrieval on blockchain and ensure the credibility of the image retrieval ***,we use digital fingerprint and watermark technologies to realize the copyright protection of images and use smart contracts to achieve fair transaction *** experimental results show that our scheme can protect image copyrights and realize a fair trading process while ensuring efficiency.

关键词： blockchain copyright-preserving trade fair trade tadors code

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Competition on robust deep learning

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National science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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BBTv2: Towards a Gradient-Free Future with Large Language Models

BBTv2: Towards a Gradient-Free Future with Large Language Mo...

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2022 Conference on Empirical Methods in Natural Language processing, EMNLP 2022

作者： Sun, Tianxiang He, Zhengfu Qian, Hong Zhou, Yunhua Huang, Xuanjing Qiu, Xipeng School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Computer Science and Technology East China Normal University China

Most downstream adaptation methods tune all or part of the parameters of pre-trained models (PTMs) through gradient descent, where the tuning cost increases linearly with the growth of the model size. By contrast, gradient-free methods only require the forward computation of the PTM to tune the prompt, retaining the benefits of efficient tuning and deployment. Though, past work on gradient-free tuning often introduces gradient descent to seek a good initialization of prompt and lacks versatility across tasks and PTMs. In this paper, we present BBTv2, an improved version of Black-Box Tuning (Sun et al., 2022b), to drive PTMs for few-shot learning. We prepend continuous prompts to every layer of the PTM and propose a divide-and-conquer gradient-free algorithm to optimize the prompts at different layers alternately. Extensive experiments across various tasks and PTMs show that BBTv2 can achieve comparable performance to full model tuning and state-of-the-art parameter-efficient methods (e.g., Adapter, LoRA, BitFit, etc.) under few-shot settings while maintaining much fewer tunable parameters. © 2022 Association for Computational Linguistics.

关键词： Gradient methods

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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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CIRCLE: scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

CIRCLE: scRNA-seq Data Clustering by Cluster-aware Iterative...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinxian Wang Weikang Jiang Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Department of Computer Science and Technolog Tongji University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware IteRative Contrastive LEarning (CIRCLE in short) for scRNA-seq data clustering, which uses an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CIRCLE consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss. First, CIRCLE extracts the feature vectors of the original and augmented data by the Transformer-encoder. Then, it computes the clustering centroids by K-means and employs the student’s t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result gets better and better. Extensive experiments on 25 real-world scRNA-seq datasets show that CIRCLE outperforms the state-of-the-art (SOTA) methods in most cases. Concretely, CIRCLE surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively. Source code is available at https://***/Alunethy/CIRCLE.

关键词： Representation learning Training Accuracy Data visualization Training data Contrastive learning Transformers Feature extraction Vectors Iterative methods

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Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

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computer Modeling in Engineering & sciences 2024年第7期140卷 863-885页

作者： Wanbo Zhang Yuqi Fan Jun Zhang Xu Ding Jung Yoon Kim School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui ProvinceHefei University of TechnologyHefei230601China Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei University of TechnologyHefei230009China College of Future Industry Gachon UniversitySeongnam13120South Korea

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide *** blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the *** is,each edge server runs both users’offloaded tasks and blockchain tasks *** that there is a trade-off between the resource allocation for MEC and blockchain ***,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based *** of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC *** this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing *** the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the *** results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： Mobile edge computing blockchain resource allocation

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RoleScan: Enhancing Social Bot Detection Using Social Role Vector 19th

RoleScan: Enhancing Social Bot Detection Using Social Role V...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wen, Wen Gao, Min Gong, Qingyuan Wang, Xin Chen, Yang 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

ISBN: (纸本)9789819623723

The increase in social media users has led to the rise of social bots, which can disrupt online environments and threaten user privacy. This study presents RoleScan, a novel framework that integrates Social Role Vector (SRV) and Graph Attention Network v2 (GATv2) to detect social bots more accurately. We are the first to formulate the social bot detection problem by considering users’ properties and social influence. RoleScan extracts user features and text features from user profiles and tweets, and uses SRV to capture the social features. SRV is based on node centrality measurement, structural hole theory, and social capital theory, offering a comprehensive view of the influence of a user within the network. By leveraging an attention mechanism, RoleScan focuses on pivotal features and connections, enhancing its ability to differentiate between bots and humans. The experimental results show that RoleScan outperforms existing models, achieving an AUC of 0.887. An ablation study further highlights the critical role of incorporating social features into the detection process. Our study not only enhances the social bot detection performance, but also offers new insights into the users’ diverse roles within social networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

关键词：

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HighlightRemover: Spatially Valid Pixel Learning for Image Specular Highlight Removal 24

HighlightRemover: Spatially Valid Pixel Learning for Image S...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Ling Ma, Yidong Jiang, Zhi He, Weilei Bao, Zhongyun Fu, Gang Xu, Wenju Xiao, Chunxia School of Computer Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial Systems Wuhan University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Computing Hong Kong Polytechnic University Hong Kong Amazon Palo Alto United States

ISBN: (纸本)9798400706868

Recently, learning-based methods have made significant progress for image specular highlight removal. However, many of these approaches treat all the image pixels uniformly, overlooking the negative impact of invalid pixels on feature reconstruction. This oversight often leads to undesirable outcomes, such as color distortion or residual highlights. In this paper, we propose a novel image specular highlight removal network called HighlightRNet, which utilizes valid pixels as references to reconstruct the highlight-free image. To achieve this, we introduce a context-aware fusion block (CFBlock) that aggregates information in four directions, effectively capturing global contextual information. Additionally, we introduce a location-aware feature transformation module (LFTModule) to adaptively learn the valid pixels for feature reconstruction, thereby avoiding information errors caused by invalid pixels. With these modules, our method can produce high-quality highlight-free results without color distortion and highlight residual. Furthermore, we develop a multiple light image-capturing system to construct a large-scale highlight dataset called NSH, which exhibits minimal misalignment in image pairs and minimal brightness variation in non-highlight regions. Experimental results on various datasets demonstrate the superiority of our method over state-of-the-art methods, both qualitatively and quantitatively. © 2024 ACM.

关键词： Pixels

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