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Damaged Wire Detection and Segmentation Method Based on Lightweight Channel Attention Mechanism and Transfer Learning

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Damaged Wire Detection and Segmentation Method Based on Ligh...

International Conference on computer Supported Cooperative Work in Design

作者： Han Wu Yunhan Lin Yaojie Chen Jianbo Li School of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real Time Industrial System Wuhan China CSSC Huangpu Wenchong Shipbuilding Company Ltd. Shanghai China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Detecting and segmenting damaged wires remains challenging due to varying lighting conditions and the constraints imposed by limited sample datasets, both of which severely degrade detection and segmentation accuracy. To address these challenges, this paper proposes a novel SECA (Squeeze-and-Excitation Channel Attention) mechanism integrated with YOLOv8n, aimed at enhancing key feature extraction under varying lighting conditions. The incorporation of transfer learning strategies further mitigates the challenges posed by insufficient sample datasets. We built an RGB dataset comprising 3,760 images representing diverse wire damage morphologies and expanded the lighting test sets to span six environments, with illuminance levels ranging from 50 to 200,000 lux, to comprehensively evaluate the model's robustness. Experimental results demonstrate that the proposed model markedly improves detection and segmentation precision as well as robustness across varying lighting conditions.

关键词： Performance evaluation Accuracy Attention mechanisms Computational modeling Wires Transfer learning Lighting Morphology Robustness Real-time systems

A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

Improving Adversarial Transferability with Neighbourhood Gradient information

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arXiv 2024年

作者： Guo, Haijing Wang, Jiafeng Chen, Zhaoyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Li, Jinglun Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai200433 China

Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack scenarios, a considerable attack performance gap between the surrogate model and the target model persists. This work focuses on enhancing the transferability of adversarial examples to narrow this performance gap. We observe that the gradient information around the clean image, i.e. Neighbourhood Gradient information, can offer high transferability. Leveraging this, we propose the NGI-Attack, which incorporates Example Backtracking and Multiplex Mask strategies, to use this gradient information and enhance transferability fully. Specifically, we first adopt Example Backtracking to accumulate Neighbourhood Gradient information as the initial momentum term. Multiplex Mask, which forms a multi-way attack strategy, aims to force the network to focus on non-discriminative regions, which can obtain richer gradient information during only a few iterations. Extensive experiments demonstrate that our approach significantly enhances the adversarial transferability. Especially, when attacking numerous defense models, we achieve an average attack success rate of 95.8%. Notably, our method can plugin with any off-the-shelf algorithm to improve their attack performance without additional time cost. Copyright © 2024, The Authors. All rights reserved.

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Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution

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arXiv 2024年

作者： Lin, Junxiong Wang, Yan Tao, Zeng Wang, Boyang Zhao, Qing Wang, Haorang Tong, Xuan Mai, Xinji Lin, Yuxuan Song, Wei Yu, Jiawen Yan, Shaoqi Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China East China University of Science and Technology Shanghai China College of Information Technology Shanghai Ocean University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue. Nonetheless, prevailing diffusion-based methodologies presently overlook the constraints imposed by degradation information on the diffusion process. Furthermore, these methods fail to consider the spatial variability inherent in the estimated blur kernel, stemming from factors such as motion jitter and out-of-focus elements in open-environment scenarios. This oversight results in a notable deviation of the image super-resolution effect from fundamental realities. To address these concerns, we introduce a framework known as Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution (SSR). Within the SSR framework, we propose a Spatially Variant Kernel Refinement (SVKR) module. SVKR estimates a Depth-Informed Kernel, which takes the depth information into account and is spatially variant. Additionally, SVKR enhance the accuracy of depth information acquired from LR images, allowing for mutual enhancement between the depth map and blur kernel estimates. Finally, we introduce the Adaptive Multi-Modal Fusion (AMF) module to align the information from three modalities: low-resolution images, depth maps, and blur kernels. This alignment can constrain the diffusion model to generate more authentic SR results. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios

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arXiv 2024年

作者： Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University China Pengcheng Laboratory China

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a limited set of scenarios where answers can be pre-determined. Furthermore, a sole emphasis on outcomes disregards the complex capabilities required for LLMs to effectively use tools. To tackle this issue, we propose ToolEyes, a fine-grained system tailored for the evaluation of the LLMs’ tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. Additionally, ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal a preference for specific scenarios and limited cognitive abilities in tool learning. Intriguingly, expanding the model size even exacerbates the hindrance to tool learning. The code and data are available at https://***/Junjie-Ye/ToolEyes. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity

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UFineBench: Towards Text-based Person Retrieval with Ultra-f...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jialong Zuo Hanyu Zhou Ying Nie Feng Zhang Tianyu Guo Nong Sang Yunhe Wang Changxin Gao National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Huawei Noah's Ark Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we con-tribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine granularity. Firstly, we construct a new dataset named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions, averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation, we also propose a spe-cial evaluation paradigm more representative of real sce-narios. It contains a new evaluation set with cross domains, cross textual granularity and cross textual styles, named UFine3C, and a new evaluation metric for accurately mea-suring retrieval ability, named mean Similarity Distribution (mSD). Moreover, we propose CFAM, a more efficient al-gorithm especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity de-coder and hard negative match mechanism. With standard in-domain evaluation, CFAM establishes competitive performance across various datasets, espe-cially on our ultra fine-grained UFine6926. Furthermore, by evaluating on UFine3C, we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at https://***/Zplusdragon/UFineBench.

关键词： Training Measurement computer vision Correlation Codes Annotations Semantics

Expectation-Constrained Programming Model for Tourist Route of Attractions Based on Uncertainty Theory 5

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Expectation-Constrained Programming Model for Tourist Route ...

5th International Conference on Advanced Electronic Materials, computers and Software Engineering, AEMCSE 2022

作者： Huang, Zheng Ning, Yufu Liu, Fengming Shandong Normal University Business School Ji'nan China Key Lab. of Intelligent Information Processing Technology and Security in Universities of Shandong Jinan250103 China Shandong Youth University of Political Science School of Information Engineering Ji'nan China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

Based on the current background of scenic spot management and tourism planning in China, this paper establishes an uncertain tourism route expectation-constrainted programming model under the framework of uncertainty theory. Finally, eight attractions in Jinan are used as practical cases to illustrate the usefulness of the model. The research results show that the proposed model can effectively alleviate the problems of unreasonable allocation of tourists' time and cost in the traditional model, and make the layout and programming of routes more reasonable and efficient. © 2022 IEEE.

关键词： computer programming

Rethinking Super-Resolution as Text-Guided Details Generation

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arXiv 2022年

作者： Ma, Chenxi Yan, Bo Lin, Qing Tan, Weimin Chen, Siming School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China School of Data Science Fudan University China

Deep neural networks have greatly promoted the performance of single image super-resolution (SISR). Conventional methods still resort to restoring the single high-resolution (HR) solution only based on the input of image modality. However, the image-level information is insufficient to predict adequate details and photo-realistic visual quality facing large upscaling factors (×8, ×16). In this paper, we propose a new perspective that regards the SISR as a semantic image detail enhancement problem to generate semantically reasonable HR image that are faithful to the ground truth. To enhance the semantic accuracy and the visual quality of the reconstructed image, we explore the multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution (TGSR) framework, which can effectively utilize the information from the text and image modalities. Different from existing methods, the proposed TGSR could generate HR image details that match the text descriptions through a coarse-to-fine process. Extensive experiments and ablation studies demonstrate the effect of the TGSR, which exploits the text reference to recover realistic images. © 2022, CC BY.

关键词： Deep neural networks

CRCC: Collaborative Relation Context Consistency on the Knowledge Graph for Recommender Systems 35

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CRCC: Collaborative Relation Context Consistency on the Know...

35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Wang, Li-E Zeng, Huachang Li, Shenghan Li, Xianxian Zhuo, Shengda Xie, Jiahua Qu, Bin Sun, Zhigang Liu, Tianran School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China College of Cyber Security of Jinan University Jinan University Guangzhou China School of Information Science and Engineering Yunnan University Kunming China

Knowledge graph (KG) as auxiliary information can solve the cold-start and data sparsity problems of recommender systems. However, most existing KG-based recommendation methods focus on how to effectively encode items with that users have interacted into entities and propagate them explicitly, but neglect the relation-level and context-level modeling of collaborative signals. Therefore, it is inevitable to incorporate some unrelated entities while utilizing a propagation strategy, which may weaken part of the recommendation performance. To address this problem, we propose a novel method named Collaborative Relation Context Consistency (CRCC). Compared with other KG-based methods, we model the relation-level and context-level of collaborative signals in a fine-grained manner. Specifically, we segment the user’s collaborative knowledge graph to learn related entity information separately to enrich the embedding of users. Moreover, CRCC links the consistency score between the items that users and neighbors have interacted with as the fusion basis, and then we consider the inherent popularity of items while incorporating consistent entities to enhance the embedding representation of items. Extensive experiments on three real-world datasets show that CRCC outperforms several compelling baselines in both CTR prediction and top-K recommendation. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Recommender systems