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Seeking a Sense of Meaning and Companionship in Life: Informal Learning on Douyin Among Chinese Older Adults

Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-24页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing China School of Information Science and Engineering East China University of Science and Technology China

This study examines how Chinese older adults leverage Douyin, a short video platform, for informal learning purposes, analyzing their usage patterns, motivations, and encountered challenges. Although Douyin was not explicitly designed with educational features, it has emerged as a significant informal learning platform for this demographic. Through a qualitative investigation comprising participant observations and semi-structured interviews with 17 participants, we reveal the distinctive learning experience that Douyin facilitates. The platform’s unique combination of short-form videos, live streaming capabilities, and interactive community features creates an engaging learning environment that particularly resonates with older adults. Our findings demonstrate that participants derive substantial social-emotional benefits beyond knowledge acquisition, including strengthened social connections, enhanced companionship, and a reinforced sense of purpose through their Douyin engagement. These social-emotional aspects emerge as crucial factors driving older adults’ selection of Douyin as their preferred learning platform. By providing comprehensive insights into older adults’ engagement with digital platforms for lifelong learning, this research offers valuable implications for HCI, particularly in understanding how technology can be optimized to support the learning needs of older populations. © 2025 Association for Computing Machinery. All rights reserved.

关键词： Social aspects

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Hi-NeRF: Hybridizing 2D Inpainting with Neural Radiance Fields for 3D Scene Inpainting 17th

Hi-NeRF: Hybridizing 2D Inpainting with Neural Radiance Fie...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Huang, Xianliang Chen, Shuhang Zhong, Zhizhou Gou, Jiajie Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Tongji University Shanghai China

ISBN: (纸本)9789819609710

Recent developments in Neural Radiance Fields (NeRF) have showcased notable progress in the synthesis of novel views. Nevertheless, there is limited research on inpainting 3D scenes using implicit representations. Traditional approaches utilizing 3D networks for direct 3D inpainting often falter in high-resolution settings, mainly due to GPU memory constraints. This paper introduces Hi-NeRF, an innovative 3D inpainting approach designed to remove arbitrary 3D objects by hybridizing 2D inpainting strategies with NeRF techniques. Recognizing that prevailing 2D inpainting methods often fail to grasp the 3D geometric intricacies of scenes, we leverage the unique capability of NeRF in capturing these structures. Additionally, we propose a multi-view perceptual loss (MVPL) to harness multi-view data, ensuring that 2D inpainting and implicit 3D representations can mutually compensate for each other. Furthermore, we refine the output from the Segment Anything Model (SAM) using image dilation to produce accurate multi-view masks. To finalize the process, we employ Instant-NGP to efficiently retrieve 3D-consistent scenes from 3D-consistent inpainted images. As there is no multi-view 3D scene datasets with corresponding masks, we construct both real-world and synthetic scenes for the multi-view 3D scene inpainting task, which serves as a benchmark dataset. Experimental results on both indoor and outdoor scenes highlight the superiority of our approach over the existing 2D inpainting methods and NeRF-based baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Exploring the Feasibility and Challenges of Treating Follow-up Patients via a Mobile Platform in China

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Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-31页

作者： Fu, Jiaojiao Zhou, Yangfan Wang, Xin Guo, Yi School of Information Science and Engineering East China University of Science and Technology School of Computer Science Fudan University China School of Computer Science Fudan University China Shanghai Key Lab. of Intelligent Information Processing China

Mobile technology is being increasingly adopted in teleconsultation for its convenience and mobility. Although widely accepted by physicians for informal online consultations, the effectiveness of mobile platforms in formal medical interventions, such as follow-up treatments, remains largely underexplored. This research presents a case study from a Chinese hospital, examining how physicians use mobile platforms to treat chronic patients, the challenges they encounter, and their overall experience. The study aims to determine whether mobile technology can enable physicians to fulfill their responsibilities in managing chronic disease follow-ups online. Through observations and interviews, we found that using mobile platforms introduces significant challenges. Physicians operate in complex and varying environments, such as workplaces, homes, and even public areas, making them struggle to access comprehensive information, communicate effectively, and maintain detailed medical records. As a result, physicians face poor working conditions, reduced efficiency, and struggle to make accurate treatment decisions. This study highlights the need for policy reforms and technological innovations to ensure sustainable teleconsultation practices on mobile platforms. It contributes to the HCI and CSCW communities by highlighting the feasibility of using mobile platforms in online chronic disease follow-ups. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： mHealth

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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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Enhancing Distributed Source Coding With Encoder-Centric Frequency Adaptation and Spatial Transformation

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IEEE Transactions on Multimedia 2025年 27卷 2582-2592页

作者： Xu, Hao Tan, Bin Chen, Yihao Hu, Die Wu, Jun Tongji University College of Electronics and Information Engineering Shanghai201804 China Jing-gangshan University College of Electronics and Information Engineering Ji'an343009 China Fudan University School of Communication Science and Engineering Shanghai200433 China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China

Current methodologies in distributed source coding have predominantly investigated decoder-focused strategies, emphasizing the alignment and exploitation of side information. This study introduces a paradigm shift by presenting an encoder-centric algorithm that conducts proactive optimization in the frequency domain. This shift is motivated by the current deep learning models' tendency to passively extract high-frequency elements, such as contours and content in the spatial domain at the encoder side, without considering the frequency characteristics of these spatial components. Unlike current trends, the proposed scheme actively selects the essential frequency components directly in the frequency domain by introducing an adaptive self-learning filter, enabling the encoder to discern and retain critical frequency components effectively and precisely. Furthermore, we align the side information in the spatial domain before feature extraction and implement an affine transformation-based alignment strategy to utilize the side information better. By leveraging the shared frequency domain components of the image pairs, the proposed algorithm adeptly learns affine coefficients to accomplish precise spatial alignment. This dual strategy of proactive encoder optimization and decoder alignment via affine transformations is highly efficient, outperforming existing state-of-the-art methods in distributed source coding when tested across two diverse datasets by an average of 0.5 dB in PSNR. © 1999-2012 IEEE.

关键词： Decoding

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liu, Guoshan Yin, Hailong Zhu, Bin Chen, Jingjing Ngo, Chong-Wah 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 Singapore Management University Singapore

ISBN: (纸本)9798331510831

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method - first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset. © 2025 IEEE.

关键词： Food ingredients

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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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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ti, Zheng Hao, Xuze Chen, Renhai College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798350368741

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios. © 2025 IEEE.

关键词： Continual Learning Knowledge Distillation Semantic Segmentation

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Sketch-based Point Cloud Generation with Diffusion Model and Pre-training Enhancement

Sketch-based Point Cloud Generation with Diffusion Model and...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Chen, Yangdong Chen, Mohan Zhang, Yuejie Feng, Rui Zhang, Tao Gao, Shang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China School of Information Technology Deakin University Victoria Australia

ISBN: (纸本)9798350368741

Diffusion models, known for their success in various generative tasks like image generation and super-resolution, are applied in this study for point cloud generation, a field that has not been extensively explored due to the complexity of point clouds. We propose a novel method using a diffusion model to generate high-quality 3D point clouds from 2D sketches. This method employs a self-supervised contrastive learning scheme to align sketch and point cloud modalities. Additionally, it incorporates a specific partition mixing strategy to integrate edge information during pre-training. Evaluated on two benchmark datasets, our method outperforms existing state-of-the-art approaches, showcasing the potential of diffusion models in point cloud generation and setting a new direction for future research. © 2025 IEEE.

关键词： Diffusion Models Point Cloud Generation Self-supervised Contrastive Learning Sketch

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MoME: Mixture of Multi-Domain Experts for Multivariate Long-Term Series Forecasting

MoME: Mixture of Multi-Domain Experts for Multivariate Long-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Xinyu Yang, Zhiheng Xu, Hao Cai, Yunqi Lu, Hong Wang, Xin Zhao, Jin Qi, Fenglin Shen, Jiajie School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Informatization Office Fudan University Shanghai China

ISBN: (纸本)9798350368741

Time series forecasting is always important, with multivariate long-term series forecasting being its most challenging task. Here, the existing methods typically learn only in a single domain and focus on optimizing model structures, leading to incomplete information mining and imprecise predictions. To address this, we propose a generalized Mixture of Multi-Domain Experts (MoME) for multivariate long-term series forecasting. Unlike most existing methods, MoME focuses on multi-perspective information mining and fusing. To this end, MoME transforms time series into the frequency and spatial domains to learn their respective representations. MoME regards variates information as embedded features and applies fast Fourier transform to the time dimension. Then it learns embedded features in the frequency domain. In spatial domain learning, MoME applies self-attention mechanism on the variates dimension to efficiently capture dependencies among multiple variates. Finally, MoME fuses the outputs from all domains, reinterprets and integrates information across multiple domains, and predicts future time series. Extensive experiments prove that MoME outperforms state-of-the-art (SOTA) methods. Code is available at: https://***/lxy-PhD2022/MoME. © 2025 IEEE.

关键词： Generalized MoE Multi-Domain learning Multivariate long-term forecasting Time series forecasting

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