# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 2 Related Work
### Attention Mechanism  
In computer vision tasks, attentional mechanisms have been extensively adopted in various fields because they allow models to focus on useful feature information. For example, using a spatial feature-enhanced attention module to further improve the backbone network's performance and apply it to pest and disease recognition in precision agriculture applications [18] and feature extraction and model learning enhanced by multiple dimensional information to further improve the accuracy and generalization of the model in the air pollutant prediction domain [19]. In the field of gaze estimation, attentional mechanisms have also been used to focus on feature information in eye images or in face images that is useful with the task of gaze estimation, and some research has been done. Based on the similarity in appearance between binocular features, the adaptive feature fusion network (AFF-Net) [20] performs adaptive fusion using the squeeze-and-excitation (SE) module [21]. Space-related research studies include FullFace [22] and AGE-Net [14]. Both use the spatial weighting mechanism [23; 24], which assigns more importance to image regions that are connected with gaze direction. Although the former network does not incorporate inter-eye dependencies, it takes into account suppressing weights for regions unrelated to gaze. The latter network, while improving prediction performance via dilated convolutions and via the adoption of spatial attention to model remote dependencies within the network, is not effective at removing the potential interference of redundant information unrelated to gaze within the feature map.  
In the field of gaze estimation, dilated convolutions are most frequently used to establish and capture long-distance dependencies between the eyes. While this technique can enhance receptive fields without introducing additional parameters, it is not effective at extracting features from small targets. Since the eyes are relatively small in face images, gaze estimation using dilated convolutions is less successful when applied directly to face images. In addition to dilated convolutions, adopting self-attention mechanisms in the network can also capture long-distance dependencies between eyes. Examples include using self-attention-augmented convolution [25] or adopting non-local modules [26]. However, adopting non-local modules produces poor real-time performance because this approach requires a lot of memory to perform complex matrix operations.  
Additionally, global pooling, or pyramid pooling [27; 28], can enhance the ability of CNNs to model remote dependencies by combining pooling layers with various poolingkernel sizes to extract global information. However, all the above methods operate on the feature map of the detection input through a square window. This limits their flexibility in capturing remote dependencies between the eyes in face images.  
For the above reasons, we propose SPMCCA-Net based on the feature-enhanced bottleneck block (SPbottleneck) and multi-criss-cross attention module (MCCA). By introducing a long strip of pooling kernels and a cross-shaped self-attention mechanism in the network, we can capture the long-distance dependencies between the eyes in face images, greatly reduce the interference of redundant information in face images on gaze estimation, and strengthen the feature extraction ability of both eyes, which is a good solution to problems that have not been solved in previous research work.