| Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures K Maeda, S Takahashi, T Ogawa, M Haseyama Computer‐Aided Civil and Infrastructure Engineering 34 (8), 654-676, 2019 | 47 | 2019 |
| Favorite video classification based on multimodal bidirectional LSTM T Ogawa, Y Sasaka, K Maeda, M Haseyama IEEE Access 6, 61401-61409, 2018 | 40 | 2018 |
| Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features K Maeda, S Takahashi, T Ogawa, M Haseyama IEEE journal of selected topics in signal processing 12 (4), 633-644, 2018 | 30 | 2018 |
| Distress classification of road structures via adaptive Bayesian network model selection K Maeda, S Takahashi, T Ogawa, M Haseyama Journal of Computing in Civil Engineering 31 (5), 04017044, 2017 | 18 | 2017 |
| Few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention Y Moroto, K Maeda, T Ogawa, M Haseyama Sensors 20 (8), 2170, 2020 | 12 | 2020 |
| User-centric visual attention estimation based on relationship between image and eye gaze data Y Moroto, K Maeda, T Ogawa, M Haseyama 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), 73-74, 2018 | 12 | 2018 |
| Automatic estimation of deterioration level on transmission towers via deep extreme learning machine based on local receptive field K Maeda, S Takahashi, T Ogawa, M Haseyama 2017 IEEE International Conference on Image Processing (ICIP), 2379-2383, 2017 | 12 | 2017 |
| Deterioration level estimation based on convolutional neural network using confidence-aware attention mechanism for infrastructure inspection N Ogawa, K Maeda, T Ogawa, M Haseyama Sensors 22 (1), 382, 2022 | 11 | 2022 |
| Distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine K Maeda, S Takahashi, T Ogawa, M Haseyama Advanced Engineering Informatics 37, 79-87, 2018 | 11 | 2018 |
| Multi-feature fusion based on supervised multi-view multi-label canonical correlation projection K Maeda, S Takahashi, T Ogawa, M Haseyama ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 10 | 2019 |
| Correlation-aware attention branch network using multi-modal data for deterioration level estimation of infrastructures N Ogawa, K Maeda, T Ogawa, M Haseyama 2021 IEEE International Conference on Image Processing (ICIP), 1014-1018, 2021 | 9 | 2021 |
| Segmentation-aware text-guided image manipulation T Haruyama, R Togo, K Maeda, T Ogawa, M Haseyama 2021 IEEE International Conference on Image Processing (ICIP), 2433-2437, 2021 | 8 | 2021 |
| Automatic Martian dust storm detection from multiple wavelength data based on decision level fusion K Maeda, T Ogawa, M Haseyama IPSJ Transactions on Computer Vision and Applications 7, 79-83, 2015 | 8 | 2015 |
| Cross-domain recommendation method based on multi-layer graph analysis with visual information T Hirakawa, K Maeda, T Ogawa, S Asamizu, M Haseyama 2021 IEEE International Conference on Image Processing (ICIP), 2688-2692, 2021 | 7 | 2021 |
| Supervised fractional-order embedding multiview canonical correlation analysis via ordinal label dequantization for image interest estimation M Matsumoto, N Saito, K Maeda, T Ogawa, M Haseyama IEEE Access 9, 21810-21822, 2021 | 7 | 2021 |
| Classification of expert-novice level using eye tracking and motion data via conditional multimodal variational autoencoder Y Akamatsu, K Maeda, T Ogawa, M Haseyama ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 6 | 2021 |
| Cross-domain semi-supervised deep metric learning for image sentiment analysis Y Liang, K Maeda, T Ogawa, M Haseyama ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 6 | 2021 |
| Multi-modal label dequantized Gaussian process latent variable model for ordinal label estimation M Matsumoto, K Maeda, N Saito, T Ogawa, M Haseyama ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 6 | 2021 |
| Distress image retrieval for infrastructure maintenance via self-Trained deep metric learning using experts’ knowledge N Ogawa, K Maeda, T Ogawa, M Haseyama IEEE Access 9, 65234-65245, 2021 | 6 | 2021 |
| Supervised fractional-order embedding geometrical multi-view CCA (SFGMCCA) for multiple feature integration K Maeda, Y Ito, T Ogawa, M Haseyama IEEE Access 8, 114340-114353, 2020 | 6 | 2020 |
Takahiro OgawaHokkaido UniversityVerified email at lmd.ist.hokudai.ac.jp
