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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Pan, Futai Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks. © 2023 IEEE.

关键词： Object detection

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intelligent Innovation Dataset on Scientific Research Outcomes and Patents

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Journal of Social Computing 2025年第1期6卷 63-73页

作者： Wu, Xinran Zou, Hui Xing, Yidan Qu, Jingjing Guo, Weiwei Fu, Xiaoming School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Shanghai University Department of Library and Information Science Shanghai200444 China Big Data Technology Co. Ltd. Shanghai200082 China Shanghai Artificial Intelligence Laboratory Shanghai200082 China Fudan University Department of Management Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Institute of Computer Science University of Göttingen Göttingen37073 Germany

Various stakeholders, such as researchers, government agencies, businesses, and research laboratories require a large volume of reliable scientific research outcomes including research articles and patent data to support their work. These data are crucial for a variety of application, such as advancing scientific research, conducting business evaluations, and undertaking policy analysis. However, collecting such data is often a time-consuming and laborious task. Consequently, many users turn to using openly accessible data for their research. However, these existing open dataset releases typically suffer from lack of relationship between different data sources and a limited temporal coverage. To address this issue, we present a new open dataset, the intelligent Innovation Dataset (IIDS), which comprises six interrelated datasets spanning nearly 120 years, encompassing paper information, paper citation relationships, patent details, patent legal statuses, and funding information. The extensive contextual and extensive temporal coverage of the IIDS dataset will provide researchers and practitioners and policy maker with comprehensive data support, enabling them to conduct indepth scientific research and comprehensive data analyses. © 2020 Tsinghua University Press.

关键词： Decentralized finance

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MVSFORMER++: REVEALING THE DEVIL IN TRANSFORMER'S DETAILS FOR MULTI-VIEW STEREO 12

MVSFORMER++: REVEALING THE DEVIL IN TRANSFORMER'S DETAILS FO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Cao, Chenjie Ren, Xinlin Fu, Yanwei School of Data Science Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China

Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms. However, existing approaches have not thoroughly investigated the profound influence of transformers on different MVS modules, resulting in limited depth estimation capabilities. In this paper, we introduce MVSFormer++, a method that prudently maximizes the inherent characteristics of attention to enhance various components of the MVS pipeline. Formally, our approach involves infusing cross-view information into the pre-trained DINOv2 model to facilitate MVS learning. Furthermore, we employ different attention mechanisms for the feature encoder and cost volume regularization, focusing on feature and spatial aggregations respectively. Additionally, we uncover that some design details would substantially impact the performance of transformer modules in MVS, including normalized 3D positional encoding, adaptive attention scaling, and the position of layer normalization. Comprehensive experiments on DTU, Tanks-and-Temples, BlendedMVS, and ETH3D validate the effectiveness of the proposed method. Notably, MVSFormer++ achieves state-of-the-art performance on the challenging DTU and Tanks-and-Temples benchmarks. Codes and models are available at https://***/maybeLx/MVSFormerPlusPlus. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Benchmarking

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Double-Mix-Net: A Multimodal Music Emotion Recognition Network with Multi-layer Feature Mixing 10th

Double-Mix-Net: A Multimodal Music Emotion Recognition Netwo...

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10th Conference on Sound and Music technology, CSMT 2023

作者： Li, Peilin Chen, Kairan Wei, Weixin Zhao, Jiahao Li, Wei School of Computer Science and Technology Fudan University Shanghai Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai Shanghai200433 China

ISBN: (纸本)9789819779611

Computational music emotion recognition (MER) is an important task that aims to recognize emotional content in music tracks. Understanding the emotional content of music can help tailor therapeutic interventions to specific emotional needs, potentially improving their effectiveness. Piano music is widely used in music therapy for depression, anxiety, and other mental health conditions. However, comparatively little work has been done on MER in piano music, which is a challenging task due to the unique difficulties in recognizing emotions in single-timbre music without vocals, especially on valence. In this paper, we propose a novel Double-Mix-Net model that utilizes the multi-layer feature mixing and multi-modal feature mixing methods to improve classification performance. Our model uses both the title of the YouTube music video and the music context to predict the emotion of a given music track. Additionally, we make full use of different granularity features through the multi-layer feature mixing method. Our proposed model achieved state-of-the-art accuracy performance of 73.8% on the Emopia dataset and is the first multi-modal network designed for piano emotion recognition. The results of our study highlight the importance of considering the music title and utilizing multi-layer feature mixing methods in piano music emotion recognition. This work helps the emotional understanding of piano music. Our proposed model can be applied in various domains such as music recommendation systems, film scoring, and game design to enhance the emotional impact of music. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Game design

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End-to-End Paired Ambisonic-Binaural Audio Rendering

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 502-513页

作者： Yin Zhu Qiuqiang Kong Junjie Shi Shilei Liu Xuzhou Ye Ju-Chiang Wang Hongming Shan Junping Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Beijing ByteDance Technology Co.Ltd. Shanghai 201102China. Chinese University of Hong Kong Hong KongChina IEEE Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

关键词： Ambisonic attention binaural rendering neural network

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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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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Jinxian Jiang, Weikang Guan, Jihong Zhou, Shuigeng Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Shanghai200433 China Tongji University Department of Computer Science and Technolog Shanghai201804 China

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： Contrastive Learning

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Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation

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Journal of computer science & technology 2023年第4期38卷 853-866页

作者：许一格邱锡鹏周浬皋黄萱菁 School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China Huawei Technologies Co. Ltd.Hangzhou 310052China

Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream *** studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and *** fine-tuning strategy itself has yet to be fully *** this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and *** self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher *** order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time *** on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or *** conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.

关键词： BERT deep learning fine-tuning natural language processing(NLP) pre-training model

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Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-23页

作者： Yu, Mengzhu Tang, Zhenjun Liang, Xiaoping Zhang, Xianquan Li, Zhixin Zhang, Xinpeng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science Fudan University Shanghai China

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances. © 2024 Copyright held by the owner/author(s).

关键词： Tensors

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SCSGNet: Spatial-Correlated and Shape-Guided Network for Breast Mass Segmentation 48

SCSGNet: Spatial-Correlated and Shape-Guided Network for Bre...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Qingqiu Xu, Jilan Yuan, Runtian Zhang, Yuejie Feng, Rui Fudan University School of Academy for Engineering and Technology Shanghai China Fudan University School of Computer Science Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9781728163277

Automatic and accurate breast mass segmentation plays a crucial role in the early diagnosis of breast cancer. However, it has been a challenging task for two main reasons: (1) Breast masses are diverse;and (2) The boundaries of masses are ambiguous. To address these problems, we propose a Spatial-Correlated and Shape-Guided Network (SCSGNet), which combines global context extraction with local boundary refinement. Specifically, the high-level features are aggregated to produce a global map as the initial guidance area, and a Series-Parallel Feature Fusion (SPFF) module is added to capture masses of different shapes and sizes. Besides, we design a Dynamic Long-range Correlation Capture (DLCC) module to capture the spatial correlation of masses at different positions. Finally, we devise a Triplet Attention Guide (TAG) module to iteratively update the feature map and refine the boundary. Experiments on two public datasets demonstrate that our method achieves superior performance over other state-of-the-art methods. © 2023 IEEE.

关键词： Iterative methods

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DO-FAM: Disentangled Non-Linear Latent Navigation For Facial Attribute Manipulation 48

DO-FAM: Disentangled Non-Linear Latent Navigation For Facial...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Yuan, Yifan Ma, Siteng Shan, Hongming Zhang, Junping Shanghai Key Lab of Intelligent Information Processing School of Computer Science China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9781728163277

Facial attribute manipulation (FAM) aims to edit the semantic attributes of facial images according to the user's requirements. Unfortunately, the majority of existing FAM methods struggle in meeting at least one of the two requirements: high reconstruction quality and high irrelevance preservation. To alleviate these two limitations, we propose a novel Disentangled nOn-linear latent navigation framework for FAM, termed DO-FAM. To promote the reconstruction quality, we leverage hypernetworks to fine-tune a pre-trained StyleGAN2 generator. To decouple entangled attributes, we propose a novel Disentangled nOn-Linear Latent transformation module, named DOLL, which consists of three components: (1) a decomposer to factorize input latent codes into two parts: attribute-related and attribute-unrelated;(2) a non-linear Latent Transformation Network (LTNet) to navigate the attribute-related latent codes to the target one with respect to the designed attribute(s);and (3) a latent classifier tasked with predicting latent codes' attributes to guide the latent code navigation. Extensive experimental results on a widely-used benchmark facial editing dataset, CelebA-HQ, demonstrate the superiority of our method over state-of-the-art methods. © 2023 IEEE.

关键词： disentangle learning Facial attribute manipulation Hypernetwork style transfer StyleGAN

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