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25th International Conference on Medical Image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a Vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Improving Automatic Piano Transcription by Refined Feature Fusion and Weighted Loss 9th

Improving Automatic Piano Transcription by Refined Feature ...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Zhao, Jiahao Wu, Yulun Wen, Liang Ma, Lianhang Ruan, Linping Wang, Wantao Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China CETHIK Group Co. Ltd Hangzhou311199 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Automatic Piano Transcription is to transcribe raw audio files into annotated piano rolls. In recent studies, jointly estimating pitch, onset, offset, and velocity of each note is commonly used. The previous state-of-the-art "Onsets and Frames" model chooses to concatenate the output of both onset and offset sub-tasks with extracted features to improve the frame-wise pitch detection, which is, however, low-efficiency in our opinion. In this paper, we proposed an improved piano transcription model by doing feature fusion and loss weighting. Our proposed model outperforms the baselines by a large margin. It also shows comparable performance with the state-of-the-art "High-Res PT" model in note metrics and outperforms it in frame metrics with an F1 score of 90.27%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musical instruments

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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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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PRIDE: Pediatric Radiological Image Diagnosis Engine Guided by Medical Domain Knowledge

PRIDE: Pediatric Radiological Image Diagnosis Engine Guided ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Yuze Pang, Shijie Wang, Zhuoyao He, Wen Wang, Yingwen Zhao, Rui-Wei Feng, Rui Zhang, Xiaobo Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China Children's Hospital Fudan University National Children's Medical Center China

ISBN: (纸本)9798350386226

Pediatric chest X-rays (CXRs) are crucial for diagnosing respiratory diseases in children. However, most deep learning models perform poorly on pediatric data due to domain gaps, as they are primarily trained on adult datasets with limited pediatric samples. Recent large models, though effective, still struggle with domain-specific terminology and specialized medical reasoning essential for pediatric diagnoses. To address these challenges, we propose PRIDE: a two-stage Pediatric Radiological Image Diagnosis Engine guided by medical knowledge. PRIDE works consistently with real clinical workflows by first gathering medical evidence from patient data, followed by applying clinical knowledge to enhance diagnostic accuracy. In the Multi-source Radiological Findings Recognition Stage, PRIDE integrates insights from generalized medical models and fine-tuned adult and pediatric radiological models to provide comprehensive findings results. In the Knowledge and Evidence-guided Diagnosis Stage, a Multimodal Large Language Model (MLLM) acts as a pediatric clinician, making diagnostic decisions using CXRs, radiological findings, demographic data, and clinical knowledge. Our evaluation on the VinDr-PCXR pediatric dataset demonstrates that PRIDE outperforms existing methods. Ablation studies further confirm the importance and effectiveness of its key components. © 2024 IEEE.

关键词： Pediatrics

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HARNESSING JOINT RAIN-/DETAIL-AWARE REPRESENTATIONS TO ELIMINATE INTRICATE RAINS

arXiv

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arXiv 2024年

作者： Ran, Wu Ma, Peirong He, Zhiquan Ren, Hao Lu, Hong School of Computer Science Fudan University China Shanghai Key Lab of Intelligent Information Processing China

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC1, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included. Copyright © 2024, The Authors. All rights reserved.

关键词： Rain

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ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios 31

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabili...

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31st International Conference on Computational Linguistics, COLING 2025

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 Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Pengcheng Laboratory China

ISBN: (纸本)9798891761964

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. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning

arXiv

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arXiv 2024年

作者： Li, Jinglun Zhou, Xinyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Chen, Zhaoyu Ge, Weifeng Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing methods for out-of-distribution (OOD) detection, a critical area where AI models encounter unseen data in real-world scenarios, rely heavily on whole-image features. These image-level features can include irrelevant information that hinders the detection of OOD samples, ultimately limiting overall performance. In this paper, we propose TagOOD, a novel approach for OOD detection that leverages visionlanguage representations to achieve label-free object feature decoupling from whole images. This decomposition enables a more focused analysis of object semantics, enhancing OOD detection performance. Subsequently, TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection. Finally, our approach efficiently detects OOD samples by calculating distance-based metrics as OOD scores between learned centers and test samples. We conduct extensive experiments to evaluate TagOOD on several benchmark datasets and demonstrate its superior performance compared to existing OOD detection methods. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, with potential applications across various tasks. Code is available at: https://***/Jarvisgivemeasuit/tagood. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE IN PIANO TRANSCRIPTION 23

HPPNET: MODELING THE HARMONIC STRUCTURE AND PITCH INVARIANCE...

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23rd International Society for Music information Retrieval Conference, ISMIR 2022

作者： Wei, Weixing Li, Peilin Yu, Yi Li, Wei School of Computer Science and Technology Fudan University China Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

ISBN: (纸本)9781732729926

While neural network models are making significant progress in piano transcription, they are becoming more resource-consuming due to requiring larger model size and more computing power. In this paper, we attempt to apply more prior about piano to reduce model size and improve the transcription performance. The sound of a piano note contains various overtones, and the pitch of a key does not change over time. To make full use of such latent information, we propose HPPNet that using the Harmonic Dilated Convolution to capture the harmonic structures and the Frequency Grouped Recurrent Neural Network to model the pitch-invariance over time. Experimental results on the MAESTRO dataset show that our piano transcription system achieves state-of-the-art performance both in frame and note scores (frame F1 93.15%, note F1 97.18%). Moreover, the model size is much smaller than the previous state-of-the-art deep learning models. © Weixing Wei, Peilin Li, Yi Yu, and Wei Li.

关键词： Harmonic analysis

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Improving Pathological Voice Detection: A Weakly Supervised Learning Method 9th

Improving Pathological Voice Detection: A Weakly Supervised ...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Wei, Weixing Wen, Liang Qian, Jiale Shan, Yufei Wang, Jun Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China CETHIK Group Co. Ltd. Hangzhou311100 China

ISBN: (纸本)9789811947025

Deep learning methods are data-driven. But for pathological voice detection, it is difficult to obtain high-quality labeled data. In this work, a weakly supervised learning Method is presented to improve the quality of existing datasets by learning sample weights and fine-grained labels. First, A convolutional neural network (CNN) is devised as the basic architecture to detect the pathological voice. Then, a proposed self-training algorithm is used to iteratively run and automatically learn the sample weights and fine-grained labels. These learned sample weights and fine-grained labels are used to train the CNN model from scratch. The experiment results on the Saarbrucken Voice database show that the diagnosis accuracy improved from 75.7 to 82.5%, with a 6.8% improvement in accuracy over the CNN models trained with the original dataset. This work demonstrates that the weakly supervised learning method can significantly improve the classification performance to distinguish pathological voice and healthy voice. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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