Alert button
Picture for Christian Micheloni

Christian Micheloni

Alert button

Is First Person Vision Challenging for Object Tracking?

Aug 31, 2021
Matteo Dunnhofer, Antonino Furnari, Giovanni Maria Farinella, Christian Micheloni

Figure 1 for Is First Person Vision Challenging for Object Tracking?
Figure 2 for Is First Person Vision Challenging for Object Tracking?
Figure 3 for Is First Person Vision Challenging for Object Tracking?
Figure 4 for Is First Person Vision Challenging for Object Tracking?
Viaarxiv icon

A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

Jul 07, 2021
Rao Muhammad Umer, Asad Munir, Christian Micheloni

Figure 1 for A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
Figure 2 for A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
Figure 3 for A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution
Viaarxiv icon

NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results

Jun 07, 2021
Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou

Figure 1 for NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Figure 2 for NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Figure 3 for NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Figure 4 for NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Viaarxiv icon

Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation

Mar 26, 2021
Matteo Dunnhofer, Niki Martinel, Christian Micheloni

Figure 1 for Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Figure 2 for Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Figure 3 for Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Figure 4 for Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Viaarxiv icon

Collaboration among Image and Object Level Features for Image Colourisation

Jan 19, 2021
Rita Pucci, Christian Micheloni, Niki Martinel

Figure 1 for Collaboration among Image and Object Level Features for Image Colourisation
Figure 2 for Collaboration among Image and Object Level Features for Image Colourisation
Figure 3 for Collaboration among Image and Object Level Features for Image Colourisation
Figure 4 for Collaboration among Image and Object Level Features for Image Colourisation
Viaarxiv icon

Is It a Plausible Colour? UCapsNet for Image Colourisation

Dec 04, 2020
Rita Pucci, Christian Micheloni, Gian Luca Foresti, Niki Martinel

Figure 1 for Is It a Plausible Colour? UCapsNet for Image Colourisation
Figure 2 for Is It a Plausible Colour? UCapsNet for Image Colourisation
Figure 3 for Is It a Plausible Colour? UCapsNet for Image Colourisation
Figure 4 for Is It a Plausible Colour? UCapsNet for Image Colourisation
Viaarxiv icon

Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset

Nov 24, 2020
Matteo Dunnhofer, Antonino Furnari, Giovanni Maria Farinella, Christian Micheloni

Figure 1 for Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset
Figure 2 for Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset
Figure 3 for Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset
Figure 4 for Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset
Viaarxiv icon

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Sep 25, 2020
Pengxu Wei, Hannan Lu, Radu Timofte, Liang Lin, Wangmeng Zuo, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Tangxin Xie, Liang Cao, Yan Zou, Yi Shen, Jialiang Zhang, Yu Jia, Kaihua Cheng, Chenhuan Wu, Yue Lin, Cen Liu, Yunbo Peng, Xueyi Zou, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Tongtong Zhao, Shanshan Zhao, Yoseob Han, Byung-Hoon Kim, JaeHyun Baek, Haoning Wu, Dejia Xu, Bo Zhou, Wei Guan, Xiaobo Li, Chen Ye, Hao Li, Haoyu Zhong, Yukai Shi, Zhijing Yang, Xiaojun Yang, Haoyu Zhong, Xin Li, Xin Jin, Yaojun Wu, Yingxue Pang, Sen Liu, Zhi-Song Liu, Li-Wen Wang, Chu-Tak Li, Marie-Paule Cani, Wan-Chi Siu, Yuanbo Zhou, Rao Muhammad Umer, Christian Micheloni, Xiaofeng Cong, Rajat Gupta, Keon-Hee Ahn, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee, Feras Almasri, Thomas Vandamme, Olivier Debeir

Figure 1 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 2 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 3 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Figure 4 for AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Viaarxiv icon

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Sep 15, 2020
Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng, Jian Cheng, Guangyang Wu, Wenyi Wang, Xiaohong Liu, Hengyuan Zhao, Xiangtao Kong, Jingwen He, Yu Qiao, Chao Dong, Xiaotong Luo, Liang Chen, Jiangtao Zhang, Maitreya Suin, Kuldeep Purohit, A. N. Rajagopalan, Xiaochuan Li, Zhiqiang Lang, Jiangtao Nie, Wei Wei, Lei Zhang, Abdul Muqeet, Jiwon Hwang, Subin Yang, JungHeum Kang, Sung-Ho Bae, Yongwoo Kim, Liang Chen, Jiangtao Zhang, Xiaotong Luo, Yanyun Qu, Geun-Woo Jeon, Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee, Steven Marty, Eric Marty, Dongliang Xiong, Siang Chen, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Haicheng Wang, Vineeth Bhaskara, Alex Levinshtein, Stavros Tsogkas, Allan Jepson, Xiangzhen Kong, Tongtong Zhao, Shanshan Zhao, Hrishikesh P S, Densen Puthussery, Jiji C V, Nan Nan, Shuai Liu, Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho, Xuehui Wang, Qiong Yan, Yuzhi Zhao, Long Chen, Jiangtao Zhang, Xiaotong Luo, Liang Chen, Yanyun Qu, Long Sun, Wenhao Wang, Zhenbing Liu, Rushi Lan, Rao Muhammad Umer, Christian Micheloni

Figure 1 for AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Figure 2 for AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Figure 3 for AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Figure 4 for AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Viaarxiv icon

Deep Iterative Residual Convolutional Network for Single Image Super-Resolution

Sep 07, 2020
Rao Muhammad Umer, Gian Luca Foresti, Christian Micheloni

Figure 1 for Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Figure 2 for Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Figure 3 for Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Figure 4 for Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
Viaarxiv icon