Zobrazeno 1 - 10
of 831
pro vyhledávání: '"Hamed, R."'
Autor:
Moskalenko, Andrey, Bryncev, Alexey, Vatolin, Dmitry, Timofte, Radu, Zhan, Gen, Yang, Li, Tang, Yunlong, Liao, Yiting, Lin, Jiongzhi, Huang, Baitao, Moradi, Morteza, Moradi, Mohammad, Rundo, Francesco, Spampinato, Concetto, Borji, Ali, Palazzo, Simone, Zhu, Yuxin, Sun, Yinan, Duan, Huiyu, Cao, Yuqin, Jia, Ziheng, Hu, Qiang, Min, Xiongkuo, Zhai, Guangtao, Fang, Hao, Cong, Runmin, Lu, Xiankai, Zhou, Xiaofei, Zhang, Wei, Zhao, Chunyu, Mu, Wentao, Deng, Tao, Tavakoli, Hamed R.
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in vario
Externí odkaz:
http://arxiv.org/abs/2409.14827
We propose an application of online hard sample mining for efficient training of Neural Radiance Fields (NeRF). NeRF models produce state-of-the-art quality for many 3D reconstruction and rendering tasks but require substantial computational resource
Externí odkaz:
http://arxiv.org/abs/2408.03193
Autor:
Jiang, Yue, Leiva, Luis A., Houssel, Paul R. B., Tavakoli, Hamed R., Kylmälä, Julia, Oulasvirta, Antti
Different types of user interfaces differ significantly in the number of elements and how they are displayed. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 pa
Externí odkaz:
http://arxiv.org/abs/2402.05202
Autor:
Zou, Nannan, Zhang, Honglei, Cricri, Francesco, Youvalari, Ramin G., Tavakoli, Hamed R., Lainema, Jani, Aksu, Emre, Hannuksela, Miska, Rahtu, Esa
Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-to-end learned video codec that introduces several architectural novel
Externí odkaz:
http://arxiv.org/abs/2112.08767
Autor:
Zhang, Honglei, Cricri, Francesco, Tavakoli, Hamed R., Zou, Nannan, Aksu, Emre, Hannuksela, Miska M.
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher
Externí odkaz:
http://arxiv.org/abs/2108.10551
Autor:
Leiva, Luis A., Xue, Yunfei, Bansal, Avya, Tavakoli, Hamed R., Köroğlu, Tuğçe, Dayama, Niraj R., Oulasvirta, Antti
Publikováno v:
Proceedings of the 22nd Intl. Conf. on Human-Computer Interaction with Mobile Devices and Services (MobileHCI), 2020
For graphical user interface (UI) design, it is important to understand what attracts visual attention. While previous work on saliency has focused on desktop and web-based UIs, mobile app UIs differ from these in several respects. We present finding
Externí odkaz:
http://arxiv.org/abs/2101.09176
Autor:
Alomari, Dana A.1, Takruri, Hamed R.1 htakruri@ju.edu.jo
Publikováno v:
Jordan Medical Journal. Jun2024, Vol. 58 Issue 2, p118-128. 11p.
Autor:
Zou, Nannan, Zhang, Honglei, Cricri, Francesco, Tavakoli, Hamed R., Lainema, Jani, Hannuksela, Miska, Aksu, Emre, Rahtu, Esa
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference time, the enco
Externí odkaz:
http://arxiv.org/abs/2007.16054
Autor:
He, Sen, Liao, Wentong, Tavakoli, Hamed R., Yang, Michael, Rosenhahn, Bodo, Pugeault, Nicolas
Automatic captioning of images is a task that combines the challenges of image analysis and text generation. One important aspect in captioning is the notion of attention: How to decide what to describe and in which order. Inspired by the successes i
Externí odkaz:
http://arxiv.org/abs/2004.14231
Autor:
Zou, Nannan, Zhang, Honglei, Cricri, Francesco, Tavakoli, Hamed R., Lainema, Jani, Aksu, Emre, Hannuksela, Miska, Rahtu, Esa
One of the core components of conventional (i.e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In this paper, we propose an end-to-end learned system for compressing vi
Externí odkaz:
http://arxiv.org/abs/2004.09226