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Ali Caglayan
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2020 – today
- 2024
- [j5]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
Attention-guided LiDAR segmentation and odometry using image-to-point cloud saliency transfer. Multim. Syst. 30(4): 188 (2024) - 2023
- [i4]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency Transfer. CoRR abs/2308.14332 (2023) - 2022
- [j4]Ali Caglayan, Nevrez Imamoglu, Ahmet Burak Can, Ryosuke Nakamura:
When CNNs meet random RNNs: Towards multi-level analysis for RGB-D object and scene recognition. Comput. Vis. Image Underst. 217: 103373 (2022) - [j3]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
SalFBNet: Learning pseudo-saliency distribution via feedback convolutional networks. Image Vis. Comput. 120: 104395 (2022) - [j2]Ali Caglayan, Nevrez Imamoglu, Ryosuke Nakamura:
MMSNet: Multi-modal scene recognition using multi-scale encoded features. Image Vis. Comput. 122: 104453 (2022) - [c9]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
SalLiDAR: Saliency Knowledge Transfer Learning for 3D Point Cloud Understanding. BMVC 2022: 584 - [i3]Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu, Weimin Wang, Ahmet Burak Can, Ryosuke Nakamura:
RGB-D SLAM Using Attention Guided Frame Association. CoRR abs/2201.12047 (2022) - 2021
- [c8]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
FBR-CNN: A Feedback Recurrent Network for Video Saliency Detection. MLSP 2021: 1-6 - [c7]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
FBNet: FeedBack-Recursive CNN for Saliency Detection. MVA 2021: 1-5 - [i2]Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura:
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks. CoRR abs/2112.03731 (2021) - 2020
- [i1]Ali Caglayan, Nevrez Imamoglu, Ahmet Burak Can, Ryosuke Nakamura:
When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition. CoRR abs/2004.12349 (2020)
2010 – 2019
- 2019
- [c6]Oguzhan Guclu, Ali Caglayan, Ahmet Burak Can:
RGB-D Indoor Mapping Using Deep Features. CVPR Workshops 2019: 59-68 - [c5]Oguzhan Guclu, Ali Caglayan, Ahmet Burak Can:
RGB-D Indoor Mapping Using Deep Features. CVPR Workshops 2019: 1248-1257 - 2018
- [b1]Ali Caglayan:
Derin öğrenme tekniklerini kullanarak rgb-d nesne tanıma (Rgb-d object recognition using deep learning techniques). Hacettepe University, Turkey, 2018 - [j1]Ali Caglayan, Ahmet Burak Can:
Volumetric Object Recognition Using 3-D CNNs on Depth Data. IEEE Access 6: 20058-20066 (2018) - [c4]Ali Caglayan, Ahmet Burak Can:
Exploiting Multi-layer Features Using a CNN-RNN Approach for RGB-D Object Recognition. ECCV Workshops (3) 2018: 675-688 - 2017
- [c3]Ali Caglayan, Ahmet Burak Can:
An Empirical Analysis of Deep Feature Learning for RGB-D Object Recognition. ICIAR 2017: 312-320 - [c2]Ali Caglayan, Ahmet Burak Can:
3D convolutional object recognition using volumetric representations of depth data. MVA 2017: 125-128 - 2013
- [c1]Ali Caglayan, Oguzhan Guclu, Ahmet Burak Can:
A Plant Recognition Approach Using Shape and Color Features in Leaf Images. ICIAP (2) 2013: 161-170
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