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Masanori Suganuma
Masanori Suganuma
Verified email at vision.is.tohoku.ac.jp - Homepage
Title
Cited by
Cited by
Year
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
7552017
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
2462019
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
1082018
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
1062019
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
1032022
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
902020
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
742020
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
512020
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
362021
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
302019
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
162021
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
162016
How can CNNs use image position for segmentation?
R Murase, M Suganuma, T Okatani
arXiv preprint arXiv:2005.03463, 2020
152020
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
152020
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
122022
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
102024
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
102024
Cross-region domain adaptation for class-level alignment
Z Wang, X Liu, M Suganuma, T Okatani
arXiv preprint arXiv:2109.06422, 2021
92021
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
82022
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
82020
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