.png){kind=icon}
返回
Frequency-spatial interaction network for gaze estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2024.102878.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Gaze estimation is a fundamental task in the field of computer vision, which determines the direction a person is looking at. With advancements in Convolutional Neural Networks (CNNs) and the availability of large-scale datasets, appearance-based models have made significant progress. Nonetheless, CNNs exhibit limitations in extracting global information from features, resulting in a constraint on gaze estimation performance. Inspired by the properties of the Fourier transform in signal processing, we propose the Frequency-Spatial Interaction network for Gaze estimation (FSIGaze), which integrates residual modules and Frequency-Spatial Synergistic (FSS) modules. To be specific, its FSS module is a dual-branch structure with a spatial branch and a frequency branch. The frequency branch employs Fast Fourier Transformation to transfer a latent representation to the frequency domain and applies adaptive frequency filter to achieve an image-size receptive field. The spatial branch, on the other hand, can extract local detailed features. Acknowledging the synergistic benefits of global and local information in gaze estimation, we introduce a Dual-domain Interaction Block (DIB) to enhance the capability of the model. Furthermore, we implement a multi-task learning strategy, incorporating eye region detection as an auxiliary task to refine facial features. Extensive experiments demonstrate that our model surpasses other state-of-the-art gaze estimation models on three three-dimensional (3D) datasets and delivers competitive results on two two-dimensional (2D) datasets.
Keyword:
Gaze estimation
Eye tracking
Fourier transform
Attention mechanism
Multitask learning
期刊
IF:
3.4
论文数:
2.3K
被引数:
3.2K
机构
引用论文
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏
Fully integrated cmos resonant cantilever sensor for bio-chemical detection in liquid environments
PREAI
分享收藏Controlled preparation of lightweight, resilient helical carbon fibers for high-performance microwave absorption and oil-water separation.png)
CARBON
CARBONIF11.6
PREAI
分享收藏
查看更多内容