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北海道大学 情報科学研究科 メディアダイナミクス研究室 Laboratory of Media Dynamics

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IEEE ICIP2021にて8件の発表を行いました!

2021年9月19日~22日にオンラインで開催された世界最高峰の画像処理に関する国際会議The 28th IEEE International Conference on Image Processing (ICIP 2021: https://2021.ieeeicip.org/)にメディアダイナミクス研究室より以下の8件を発表しました!
IEEE ICIP2022は、フランスボルドーで開催予定です。

[1] Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama, “INTERPRETABLE REPRESENTATION LEARNING ON NATURAL IMAGE DATASETS VIA RECONSTRUCTION IN VISUAL-SEMANTIC EMBEDDING SPACE”
[2] Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “DEEP METRIC NETWORK VIA HETEROGENIOUS SEMANTICS FOR IMAGE SENTIMENT ANALYSIS”
[3] Tomoki Haruyama, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “SEGMENTATION-AWARE TEXT-GUIDED IMAGE MANIPULATION”
[4] Kaito Hirasawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “TIME-LAG AWARE MULTI-MODAL VARIATIONAL AUTOENCODER USING BASEBALL VIDEOS AND TWEETS FOR PREDICTION OF IMPORTANT SCENES”
[5] Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “INTEREST LEVEL ESTIMATION VIA MULTI-MODAL GAUSSIAN PROCESS LATENT VARIABLE FACTORIZATION”
[6] Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “CORRELATION-AWARE ATTENTION BRANCH NETWORK USING MULTI-MODAL DATA FOR DETERIORATION LEVEL ESTIMATION OF INFRASTRUCTURES”
[7] Taisei Hirakawa, Keisuke Maeda, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama, “CROSS-DOMAIN RECOMMENDATIN METHOD BASED ON MULTI-LAYER GRAPH ANALYSIS WITH VISUAL INFORMATIN”
[8] Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, “FEW-SHOT PERSONALIZED SALIENCY PREDICTION USING PERSON SIMILARITY BASED ON COLLABORATIVE MULTI-OUTPUT GAUSSIAN PROCESS REGRESSION”

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