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Search: "[ keyword: Multimodal ]" (13)
    A Study on the Multimodal Fraud Transaction Detection Model Based on Financial Transactions and Signature Activities
    Chan-sik Sung, Kwan-yeol Park, Tae-yang Park The Transactions of the Korea Information Processing Society, Vol. 15, No. 2, pp. 169-179, Feb. 2026
    https://doi.org/10.3745/TKIPS.2026.15.2.169
    Keywords: pace-time attention learning, context embedding learning, time series correlation mismatch learning, fusion learning, multimodal data learning


    Performance Analysis of Video–Audio Action Recognition Using a Cross-Attention-Based Multimodal Fusion Architecture
    Jun Hwa Kim The Transactions of the Korea Information Processing Society, Vol. 15, No. 2, pp. 113-120, Feb. 2026
    https://doi.org/10.3745/TKIPS.2026.15.2.113
    Keywords: multimodal learning, Action Recognition, audio-visual fusion, Cross attention, Transformer


    MLSQ: A Multimodal-based System for Learning Material Summarization and Question Generation
    Geonwoo Yu, Sangyoon Lee, Jinyoung Ahn, Minha Woo, Sugyeong Kim, Jungoo Lee, Hyeonwoo Choi, Yaeran Kim, Woonghee Lee The Transactions of the Korea Information Processing Society, Vol. 14, No. 12, pp. 1004-1015, Dec. 2025
    10.3745/TKIPS.2025.14.12.1004
    Keywords: Multimodal, Automatic Question Generation, Learning Material Summarization, Large Language Model, Optical Character Recognition


    Research on the Use of Multimodal Data for Detecting Emergency Situations Involving Elderly People Living Alone
    Suyeon Lim, Seongbok Baik, Yong-Geun Hong The Transactions of the Korea Information Processing Society, Vol. 14, No. 11, pp. 950-959, Nov. 2025
    https://doi.org/10.3745/TKIPS.2025.14.11.950
    Keywords: Multimodal Data, Anomaly Detection, Early Fusion


    Label Differential Privacy Study for Privacy Protection in Multimodal Contrastive Learning Model
    Youngseo Kim, Minseo Yu, Younghan Lee, Ho Bae The Transactions of the Korea Information Processing Society, Vol. 14, No. 5, pp. 289-296, May. 2025
    https://doi.org/10.3745/TKIPS.2025.14.5.289  
    Keywords: Differential privacy, multimodal deep learning, contrastive learning, data privacy


    SViT: A Novel Multimodal Learning Approach for Ship Distance Estimation via Time-Series Data Visualization
    Sun Choi, Jeongmin Choi, Hyunbae Chang, Jhonghyun An The Transactions of the Korea Information Processing Society, Vol. 14, No. 3, pp. 203-213, Mar. 2025
    https://doi.org/10.3745/TKIPS.2025.14.3.203
    Keywords: Underwater Ship Estimation, multivariate time series forecasting, Multimodal, Vision Transformer (ViT)


    Pedestrian Road-Crossing Prediction and Safety Enhancement Using Fine-tuned VideoLLaMA2
    Sung Hun Kim, Je-Seok Ham, Jinyoung Moon The Transactions of the Korea Information Processing Society, Vol. 14, No. 1, pp. 32-40, Jan. 2025
    https://doi.org/10.3745/TKIPS.2025.14.1.32
    Keywords: Pedestrian Road-Crossing Prediction, Pedestrian Safety, Multimodal Large Language Model, VideoLLaMA2, Fine-Tuning


    Contrastive Learning Based on Modality Reflection View for Accurate Multimedia Recommendation
    Sohee Ban, Taeri Kim, Sang-Wook Kim The Transactions of the Korea Information Processing Society, Vol. 13, No. 11, pp. 637-644, Nov. 2024
    https://doi.org/10.3745/TKIPS.2024.13.11.637
    Keywords: Multimedia Recommendation, contrastive learning, Multimodal Features


    Efficient Emotion Classification Method Based on Multimodal Approach Using Limited Speech and Text Data
    Mirr Shin, Youhyun Shin The Transactions of the Korea Information Processing Society, Vol. 13, No. 4, pp. 174-180, Apr. 2024
    https://doi.org/10.3745/TKIPS.2024.13.4.174
    Keywords: Artificial intelligence, Natural Language Processing, speech recognition, Multimodal, Emotion classification


    Development of Gas Type Identification Deep-learning Model through Multimodal Method
    Seo Hee Ahn, Gyeong Yeong Kim, Dong Ju Kim KIPS Transactions on Software and Data Engineering, Vol. 12, No. 12, pp. 525-534, Dec. 2023
    https://doi.org/10.3745/KTSDE.2023.12.12.525
    Keywords: AI, Deep Learning, Multimodal, Gas Detection, Gas Identification


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