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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhan Tong Zhan Wu Yang Yang Weilong Mao Shijie Wang Yinsheng Li Yang Chen Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Laboratory of Image Science and Technology School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.

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Concept Evolution Detection Based on Noise Reduction Soft Boundary

SSRN

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SSRN 2022年

作者： Guo, Husheng Li, Hai Wang, Wenjian School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Lab of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Concept evolution detection is an important but difficult problem in streaming data analysis, and further the noise may seriously restrict the detection performance improvement. This paper proposed a concept evolution detection method based on noise reduction soft boundary (NRSB). Firstly, the noise reduction soft boundaries are defined to reduce the negative effect of noise samples near category boundary based on the introducing of extension distance, intension distance and corresponding noise reduction process. Then corresponding to each category, the sample space is divided into in-class, mixed-class and out-of-class areas. For online new coming samples, three strategies automatic labeling by model, active labeling by expert and multi-class collaborative labeling are adopted according to different areas they located respectively. Automatic model labeling does not need to update the model, which may improve the learning efficiency. Expert active labeling will help to process difficult samples in mixed-class area. Multi-class collaborative labeling can change the label belonging of samples dynamically during the process of online learning. The experimental results show that the proposed method can reflect the character of dynamic evolution of streaming data well and detect the concept evolution effectively. © 2022, The Authors. All rights reserved.

关键词： Noise abatement

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Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

arXiv

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arXiv 2024年

作者： Jiang, Jianping Zhou, Xinyu Wang, Bingxuan Deng, Xiaoming Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand – the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Design of an Integrated A/D Converter for Dissolved Oxygen Sensor Interface in Aquaculture

Design of an Integrated A/D Converter for Dissolved Oxygen S...

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2022 International Conference on Mechanisms and Robotics, ICMAR 2022

作者： Zuo, Qu Tian, YunChen Ma, GuoQiang Li, ZhenZhong Sun, ZhenLong School of Computer and Information Engineering Tianjin Agricultural University Tianjin300384 China Tianjin Key Lab of Aqua-ecology and aquaculture Tianjin300384 China Shandong Dongrun Instrument Science and Technology Co. Ltd Shandong264003 China Nantong Longyang Aquaculture Co. Ltd Jiangsu226600 China

ISBN: (纸本)9781510657328

China is a big country in aquaculture, but not a strong country, and the need for transformation and upgrading is urgent. The rapid development of intensive and large-scale aquaculture has put forward new requirements for online monitoring of water quality and automatic control of equipment. The current water quality monitoring IoT system has multiple brands of dissolved oxygen sensor equipment is not compatible, the application cost of replacing the dissolved oxygen sensor is high. In this research, an integrated A/D converter device for dissolved oxygen sensor interface is designed, without changing the premise of water quality monitoring IOT system architecture, to achieve different brands of dissolved oxygen sensor "plug and play". This research analyzes the mainstream brand dissolved oxygen sensors and designs the system architecture of the integrated A/D converter device. The integrated A/D converter device takes STM32F103C8T6 as the control core, is composed of power module, processing chip module, data storage module, signal acquisition module and data transmission module, and introduces the function and structure of each module in detail. Then a prototype was built, and the testing of the prototype was completed by building a test platform. The test result shows that the prototype can operate normally. Finally, areas for improvement and next works for the article are given. © 2022 SPIE.

关键词： Data acquisition

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Balanced Contrastive Learning for Long-Tailed Visual Recognition

arXiv

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arXiv 2022年

作者： Zhu, Jianggang Wang, Zheng Chen, Jingjing Chen, Yi-Ping Phoebe 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 Department of Computer Science and Information Technology La Trobe University Australia

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied, methods for representing imbalanced data are under-explored. In this paper, we focus on representation learning for imbalanced data. Recently, supervised contrastive learning has shown promising performance on balanced data recently. However, through our theoretical analysis, we find that for long-tailed data, it fails to form a regular simplex which is an ideal geometric configuration for representation learning. To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, we propose a novel loss for balanced contrastive learning (BCL). Compared with SCL, we have two improvements in BCL: class-averaging, which balances the gradient contribution of negative classes;class-complement, which allows all classes to appear in every mini-batch. The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. Equipped with BCL, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on long-tailed benchmark datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at this URL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction

arXiv

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arXiv 2024年

作者： Fu, Jiyuan Chen, Zhaoyu Jiang, Kaixun Guo, Haijing Wang, Jiafeng Gao, Shuyong Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely studies the transferability of attacks on VLP models, resulting in a substantial performance gap from white-box attacks. We observe that prior work overlooks the interaction mechanisms between modalities, which plays a crucial role in understanding the intricacies of VLP models. In response, we propose a novel attack, called Collaborative Multimodal Interaction Attack (CMI-Attack), leveraging modality interaction through embedding guidance and interaction enhancement. Specifically, attacking text at the embedding level while preserving semantics, as well as utilizing interaction image gradients to enhance constraints on perturbations of texts and images. Significantly, in the image-text retrieval task on Flickr30K dataset, CMI-Attack raises the transfer success rates from ALBEF to TCL, CLIPViT and CLIPCNN by 8.11%-16.75% over state-of-the-art methods. Moreover, CMI-Attack also demonstrates superior performance in cross-task generalization scenarios. Our work addresses the underexplored realm of transfer attacks on VLP models, shedding light on the importance of modality interaction for enhanced adversarial robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Cascading Enhancement Representation for Face Anti-Spoofing

SSRN

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SSRN 2023年

作者： Ma, Yimei Dong, Yangwei Qian, Jianjun Wong, Wai Keung Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong

Face anti-spoofing (FAS) is the first security line of defense in face recognition system. The majority of current methods focus on distinguishing the live faces from spoof faces by designing adaptive network with auxiliary information to enhance the feature discrimination, which shows that how to achieve the discriminative representation is also vital to solve FAS task. In this paper, motivated by the idea of cascading enhancement, we propose a novel cascading enhancement representation network (CERN) for effective FAS. Specifically, the CERN utilizes two branches to achieve multi-level feature in cascading enhancement feature extraction stage. The first branch employs the backbone network to concatenate the multi-scale feature in conjunction with attention modules. The second branch utilizes the shared attention modules to enhance the input space for learning the multi-level refinement features. In cascading enhancement feature fusion stage, we transmit the high-level feature to the middle level (mid-level) for enhancing the mid-level representation. The novel mid-level feature is then used to enhance the low-level feature. Moreover, the weight map learning scheme is proposed to further enhance discrimination of the predicted binary map. Additionally, we use meta learning to extend our CERN for solving cross-database testing. Experiments on five benchmark databases demonstrate the effectiveness of our methods against the state-of-art methods. © 2023, The Authors. All rights reserved.

关键词： Face recognition

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General Compression Framework for Efficient Transformer Object Tracking

arXiv

引用

arXiv 2024年

作者： Hong, Lingyi Li, Jinglun Zhou, Xinyu Yan, Shilin Guo, Pinxue Jiang, Kaixun Chen, Zhaoyu Gao, Shuyong Zhang, Wei Lu, Hong Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China

Transformer-based trackers have established a dominant role in the field of visual object tracking. While these trackers exhibit promising performance, their deployment on resource-constrained devices remains challenging due to inefficiencies. To improve the inference efficiency and reduce the computation cost, prior approaches have aimed to either design lightweight trackers or distill knowledge from larger teacher models into more compact student trackers. However, these solutions often sacrifice accuracy for speed. Thus, we propose a general model compression framework for efficient transformer object tracking, named CompressTracker, to reduce the size of a pre-trained tracking model into a lightweight tracker with minimal performance degradation. Our approach features a novel stage division strategy that segments the transformer layers of the teacher model into distinct stages, enabling the student model to emulate each corresponding teacher stage more effectively. Additionally, we also design a unique replacement training technique that involves randomly substituting specific stages in the student model with those from the teacher model, as opposed to training the student model in isolation. Replacement training enhances the student model’s ability to replicate the teacher model’s behavior. To further forcing student model to emulate teacher model, we incorporate prediction guidance and stage-wise feature mimicking to provide additional supervision during the teacher model’s compression process. Our framework CompressTracker is structurally agnostic, making it compatible with any transformer architecture. We conduct a series of experiment to verify the effectiveness and generalizability of CompressTracker. Our CompressTracker-4 with 4 transformer layers, which is compressed from OSTrack, retains about 96% performance on LaSOT (66.1% AUC) while achieves 2.17× speed up. © 2024, CC BY.

关键词： Students

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P3S-Diffusion:A Selective Subject-driven Generation Framework via Point Supervision

arXiv

引用

arXiv 2024年

作者： Hu, Junjie Gao, Shuyong Hong, Lingyi Wang, Qishan Zhao, Yuzhou Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selecting the content in a given reference image still poses challenges, especially when selecting the similar subjects in an image (e.g., two different dogs). Some methods attempt to use text prompts or pixel masks to isolate specific elements. However, text prompts often fall short in precisely describing specific content, and pixel masks are often expensive. To address this, we introduce P3S-Diffusion, a novel architecture designed for context-selected subject-driven generation via point supervision. P3S-Diffusion leverages minimal cost label (e.g., points) to generate subject-driven images. During fine-tuning, it can generate an expanded base mask from these points, obviating the need for additional segmentation models. The mask is employed for inpainting and aligning with subject representation. The P3S-Diffusion preserves fine features of the subjects through Multi-layers Condition Injection. Enhanced by the Attention Consistency Loss for improved training, extensive experiments demonstrate its excellent feature preservation and image generation capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.

arXiv

引用

arXiv 2024年

作者： Wang, Haoran Mai, Xinji Tao, Zeng Tong, Xuan Lin, Junxiong Wang, Yan Yu, Jiawen Wang, Boyang Yan, Shaoqi Zhao, Qing Zhou, Ziheng Gao, Shuyong Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by training on voluminous datasets. Yet, the DFER datasets encompass a substantial volume of noise data. Noise arises from low-quality captures that defy logical labeling, and instances that suffer from mislabeling due to annotation bias, engendering two principal types of uncertainty: the uncertainty regarding data usability and the uncertainty concerning label reliability. Addressing the two types of uncertainty, we have meticulously crafted a two-stage framework aiming at Seeking Certain data In extensive Uncertain data (SCIU). This initiative aims to purge the DFER datasets of these uncertainties, thereby ensuring that only clean, verified data is employed in training processes. To mitigate the issue of low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which assesses sample weights and prunes those deemed unusable due to their low weight. For samples with incorrect annotations, the Fine-Grained Correction (FGC) stage evaluates prediction stability to rectify mislabeled data. Moreover, SCIU is conceived as a universally compatible, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methodologies. Rigorous experiments across prevalent DFER datasets and against numerous benchmark methods substantiates SCIU’s capacity to markedly elevate performance metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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