Using lightweight models as backbone networks in gaze estimation tasks often results in significant performance degradation. The main reason is that the number of feature channels in lightweight networks is usually small, which makes the model expression ability limited. In order to improve the performance of lightweight models in gaze estimation tasks, a network model named Multitask-Gaze is proposed. The main components of Multitask-Gaze include Unidirectional Convolution (UC), Spatial and Channel Attention (SCA), Global Convolution Module (GCM), and Multi-task Regression Module(MRM). UC not only significantly reduces the number of parameters and FLOPs, but also extends the receptive field and improves the long-distance modeling capability of the model, thereby improving the model performance. SCA highlights gaze-related features and suppresses gaze-irrelevant features. The GCM replaces the pooling layer and avoids the performance degradation due to information loss. MRM improves the accuracy of individual tasks and strengthens the connections between tasks for overall performance improvement. The experimental results show that compared with the State-of-the-art method SUGE, the performance of Multitask-Gaze on MPIIFaceGaze and Gaze360 datasets is improved by 1.71% and 2.75%, respectively, while the number of parameters and FLOPs are significantly reduced by 75.5% and 86.88%.

本研究解决了轻量级模型在凝视估计任务中性能显著下降的问题，主要是由于特征通道数量不足。提出的Multitask-Gaze网络模型采用单向卷积、空间和通道注意力等新方法，提高了模型的表征能力。实验结果显示，与当前最先进的方法相比，Multitask-Gaze在MPIIFaceGaze和Gaze360数据集上分别提升了1.71%和2.75%的性能，同时参数和FLOPs大幅减少75.5%和86.88%。

单向卷积的多任务凝视估计