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ORCIDMárton Szemenyei
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2020 – today
- 2024
[c13]Tamás Márk Fehér
, Márton Szemenyei:
Adversarial 3D Objects Against Monocular Depth Estimators. ICMLT 2024: 138-142- [c12]Nándor Kofaragá, Márton Szemenyei:
Multi-view object detection using NeRF and YOLO. MOST 2024: 12-23 - [c11]Ádám Kürti, Márton Szemenyei:
Automatic Traffic Monitoring using Spatial Transformer Network and Monocular Depth Estimation. MOST 2024: 34-41 - 2023
- [j5]Blanka Bencsik, István Reményi, Márton Szemenyei, János Botzheim:
Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data. Sensors 23(4): 1874 (2023) - [c10]Changlu Guo, Jiangyan Dai, Márton Szemenyei, Yugen Yi:
Channel Attention Separable Convolution Network for Skin Lesion Segmentation. ICONIP (3) 2023: 385-398 - [i8]Changlu Guo, Jiangyan Dai, Márton Szemenyei, Yugen Yi:
Channel Attention Separable Convolution Network for Skin Lesion Segmentation. CoRR abs/2309.01072 (2023) - 2022
- [j4]Márton Szemenyei, Vladimir Estivill-Castro:
Fully neural object detection solutions for robot soccer. Neural Comput. Appl. 34(24): 21419-21432 (2022) - [c9]Solt Skribanek, Márton Szemenyei, Róbert Moni:
Semantically Consistent Sim-to-Real Image Translation with Neural Networks. ICAISC (2) 2022: 68-79 - [i7]Zoltán Lorincz, Márton Szemenyei, Róbert Moni:
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer. CoRR abs/2206.10797 (2022) - 2021
- [b1]Márton Szemenyei:
3D Shape Recognition Methods for Tangible User Interfaces. Budapest University of Technology and Economics, Hungary, 2021 - [j3]Márton Szemenyei, Patrik Reizinger:
Handling Realistic Noise in Multi-Agent Systems with Self-Supervised Learning and Curiosity. J. Artif. Intell. Soft Comput. Res. 12(2): 135-148 (2021) - [c8]Changlu Guo, Márton Szemenyei, Yangtao Hu, Wenle Wang, Wei Zhou, Yugen Yi:
Channel Attention Residual U-Net for Retinal Vessel Segmentation. ICASSP 2021: 1185-1189 - 2020
- [c7]Changlu Guo, Márton Szemenyei, Yugen Yi, Ying Xue, Wei Zhou, Yangyuan Li:
Dense Residual Network for Retinal Vessel Segmentation. ICASSP 2020: 1374-1378 - [c6]Patrik Reizinger, Márton Szemenyei:
Attention-Based Curiosity-Driven Exploration in Deep Reinforcement Learning. ICASSP 2020: 3542-3546 - [c5]Changlu Guo, Márton Szemenyei, Yugen Yi, Wei Zhou, Haodong Bian:
Residual Spatial Attention Network for Retinal Vessel Segmentation. ICONIP (1) 2020: 509-519 - [c4]Changlu Guo, Márton Szemenyei, Yugen Yi, Wenle Wang, Buer Chen, Changqi Fan:
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. ICPR 2020: 1236-1242 - [i6]Changlu Guo, Márton Szemenyei, Yugen Yi, Wenle Wang, Buer Chen, Changqi Fan:
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. CoRR abs/2004.03696 (2020) - [i5]Changlu Guo, Márton Szemenyei, Yugen Yi, Ying Xue, Wei Zhou, Yangyuan Li:
Dense Residual Network for Retinal Vessel Segmentation. CoRR abs/2004.03697 (2020) - [i4]Changlu Guo, Márton Szemenyei, Yugen Yi, Wei Zhou:
Channel Attention Residual U-Net for Retinal Vessel Segmentation. CoRR abs/2004.03702 (2020) - [i3]Changlu Guo, Márton Szemenyei, Yugen Yi, Wei Zhou, Haodong Bian:
Residual Spatial Attention Network for Retinal Vessel Segmentation. CoRR abs/2009.08829 (2020)
2010 – 2019
- 2019
- [c3]Changlu Guo, Márton Szemenyei, Yang Pei, Yugen Yi, Wei Zhou:
SD-Unet: A Structured Dropout U-Net for Retinal Vessel Segmentation. BIBE 2019: 439-444 - [c2]Márton Szemenyei, Vladimir Estivill-Castro:
ROBO: Robust, Fully Neural Object Detection for Robot Soccer. RoboCup 2019: 309-322 - [i2]Patrik Reizinger, Márton Szemenyei:
Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning. CoRR abs/1910.10840 (2019) - [i1]Márton Szemenyei, Vladimir Estivill-Castro:
ROBO: Robust, Fully Neural Object Detection for Robot Soccer. CoRR abs/1910.10949 (2019) - 2018
- [j2]Márton Szemenyei, Ferenc Vajda:
3D Object Detection and Scene Optimization for Tangible Augmented Reality. Period. Polytech. Electr. Eng. Comput. Sci. 62(2): 25-37 (2018) - [c1]Márton Szemenyei, Vladimir Estivill-Castro:
Real-Time Scene Understanding Using Deep Neural Networks for RoboCup SPL. RoboCup 2018: 96-108 - 2017
- [j1]Márton Szemenyei, Ferenc Vajda:
Dimension reduction for objects composed of vector sets. Int. J. Appl. Math. Comput. Sci. 27(1): 169 (2017)
Coauthor Index
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