| A Genetic Programming Approach to Designing Convolutional Neural Network Architectures M Suganuma, S Shirakawa, T Nagao Proceedings of the Genetic and Evolutionary Computation Conference 2017, 497-504, 2017 | 508 | 2017 |
| Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration X Liu, M Suganuma, Z Sun, T Okatani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 102 | 2019 |
| Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search M Suganuma, M Ozay, T Okatani International Conference on Machine Learning (ICML), 2018 | 72 | 2018 |
| Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions M Suganuma, X Liu, T Okatani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 54 | 2019 |
| Evolution of deep convolutional neural networks using cartesian genetic programming M Suganuma, M Kobayashi, S Shirakawa, T Nagao Evolutionary computation 28 (1), 141-163, 2020 | 29 | 2020 |
| Hyperparameter-free out-of-distribution detection using softmax of scaled cosine similarity E Techapanurak, M Suganuma, T Okatani arXiv:1905.10628 (Accepted to ACCV), 2019 | 15 | 2019 |
| Hierarchical feature construction for image classification using genetic programming M Suganuma, D Tsuchiya, S Shirakawa, T Nagao IEEE International Conference on Systems, Man, and Cybernetics (SMC), 001423 …, 2016 | 14 | 2016 |
| Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks VQ Nguyen, M Suganuma, T Okatani arXiv preprint arXiv:2106.00596 (IJCAI 2021), 2021 | 13 | 2021 |
| Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs VQ Nguyen, M Suganuma, T Okatani European Conference on Computer Vision (ECCV), 2020 | 13 | 2020 |
| Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarity E Techapanurak, M Suganuma, T Okatani Proceedings of the Asian Conference on Computer Vision (ACCV), 2020 | 11 | 2020 |
| How can CNNs use image position for segmentation? R Murase, M Suganuma, T Okatani arXiv preprint arXiv:2005.03463, 2020 | 7 | 2020 |
| Efficient attention mechanism for handling all the interactions between many inputs with application to visual dialog VQ Nguyen, M Suganuma, T Okatani arXiv:1911.11390 (European Conference on Computer Vision (ECCV)), 2019 | 5 | 2019 |
| Designing convolutional neural network architectures using cartesian genetic programming M Suganuma, S Shirakawa, T Nagao Deep Neural Evolution, 185-208, 2020 | 4 | 2020 |
| Restoring images with unknown degradation factors by recurrent use of a multi-branch network X Liu, M Suganuma, X Luo, T Okatani arXiv preprint arXiv:1907.04508, 2019 | 4* | 2019 |
| 決定木および決定ネットワークによる画像分類過程の説明文の自動生成 崎津実穂， 菅沼雅徳， 土屋大樹， 長尾智晴 情報処理学会論文誌数理モデル化と応用 (TOM) 9 (1), 43-52, 2016 | 4 | 2016 |
| Removal of Image Obstacles for Vehicle-Mounted Surrounding Monitoring Cameras by Real-Time Video Inpainting Y Hirohashi, K Narioka, M Suganuma, X Liu, Y Tamatsu, T Okatani IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW …, 2020 | 3 | 2020 |
| Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors Y Hosoya, M Suganuma, T Okatani WACV 2020, 2019 | 3 | 2019 |
| Generative adversarial network for visualizing convolutional network M Kobayashi, M Suganuma, T Nagao 2017 IEEE 10th International Workshop on Computational Intelligence and …, 2017 | 2 | 2017 |
| Acquiring grasp strategies for a multifingered robot hand using evolutionary algorithms C Hirayama, T Watanabe, S Kawabata, M Suganuma, T Nagao 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017 | 2 | 2017 |
| Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes W Song, M Suganuma, X Liu, N Shimobayashi, D Maruta, T Okatani IEEE International Conference on Computer Vision (ICCV), 6029-6038, 2021 | 1 | 2021 |
