| 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 | 773 | 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 | 275 | 2019 |
| GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features VQ Nguyen, M Suganuma, T Okatani arXiv:2207.09666 (Accepted to ECCV'22), 2022 | 131 | 2022 |
| 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 | 111 | 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 | 109 | 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 | 95 | 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 | 89 | 2020 |
| 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 | 51 | 2020 |
| 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 | 37 | 2021 |
| 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 | 31 | 2019 |
| Improving visual question answering for bridge inspection by pre‐training with external data of image–text pairs T Kunlamai, T Yamane, M Suganuma, PJ Chun, T Okatani Computer‐Aided Civil and Infrastructure Engineering 39 (3), 345-361, 2024 | 22 | 2024 |
| Contextual affinity distillation for image anomaly detection J Zhang, M Suganuma, T Okatani Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 19 | 2024 |
| 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 | 19 | 2021 |
| Designing convolutional neural network architectures using cartesian genetic programming M Suganuma, S Shirakawa, T Nagao Deep Neural Evolution: Deep Learning with Evolutionary Computation, 185-208, 2020 | 17 | 2020 |
| 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 | 16 | 2016 |
| How can CNNs use image position for segmentation? R Murase, M Suganuma, T Okatani arXiv preprint arXiv:2005.03463, 2020 | 15 | 2020 |
| k‐Means Clustering for Prediction of Tensile Properties in Carbon Fiber‐Reinforced Polymer Composites H Kurita, M Suganuma, Y Wang, F Narita Advanced Engineering Materials 24 (5), 2101072, 2022 | 14 | 2022 |
| Bridging the gap from asymmetry tricks to decorrelation principles in non-contrastive self-supervised learning KJ Liu, M Suganuma, T Okatani Advances in Neural Information Processing Systems 35, 19824-19835, 2022 | 11 | 2022 |
| Sbcformer: lightweight network capable of full-size imagenet classification at 1 fps on single board computers X Lu, M Suganuma, T Okatani Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 9 | 2024 |
| Cross-region domain adaptation for class-level alignment Z Wang, X Liu, M Suganuma, T Okatani arXiv preprint arXiv:2109.06422, 2021 | 9 | 2021 |