# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 2 Related Work  
### Appearance-Based Gaze Estimation  
In order to combine head-pose information with extracted eye features and learn gaze, [PERSON] [8] used a shallow network architecture and monocular images as input. The gaze error was then further reduced via a 13-layer convolutional neural network called GazeNet [9]. These approaches are limited by their reliance on single images as input; under these impoverished input conditions, it is extremely challenging to reduce errors in gaze estimation.  
[PERSON] and [PERSON] proposed the Dilated-Net [10], which uses face images and binocular images as inputs. Their network relies on dilation convolution to extract high-level eye features, allowing it to capture small variations between eyes. These authors went on to propose a network for gaze estimation with dilation and decomposition (GEDDnet) [11], which combined gaze decomposition and dilation convolution for improved estimation. Since the eyes are asymmetric, [PERSON] et al. [12] proposed the asymmetric regression-evaluation network (ARE-Net) to extract different features from the eyes. In building on their previous work, these authors [13] further proposed a face-based asymmetric regression-evaluation network (FARE-Net) to optimize gaze estimation by incorporating differences between eyes. Based on these studies, [PERSON] et al. [14] proposed two networks: gaze estimation using dilated and differential layer networks (I2D-Net) and attention-based gaze estimation networks (AGE-Net). The former network eliminates features that are unrelated to estimation of the visual line of gaze via a differential layer and preserves relevant features from both eyes by extracting and recording absolute differences between left and right eye features. The latter network adds an attention branch to the feature extraction branch for both eyes, thus significantly mitigating the impact of variations in illumination, individual appearance, and head pose.  
Some studies only adopted face images as feature extraction input, which demonstrates improved performance compared to approaches that only used eye images. However, face images contain redundant information [4]. [PERSON] et al. [15] estimated the general direction of gaze from face images and subsequently refined their estimation using eye features. In later work, these authors proposed using a transformer in gaze estimation (GazeTR) [16] and investigated the performance of the transformer by generating feature maps after passing face images through a convolutional neural network. To improve network capability for capturing global relations, they passed the feature matrix to the encoder in the transformer stage. These studies attempt to increase the effect of gaze estimation on face images through detailed information from both eyes or to increase attention to eye regions of the face through processes of self-attention; however, none of them take into account minimizing the interference from redundant information. The most recent work on gaze estimation involved L2 CS-Net (L2 loss + cross-entropy los + softmax layer network) [17]. This network was introduced to predict 3D gaze direction in unconstrained environments. For each gaze angle, the network creators applied two different losses, each of which was a linear combination of regression and classification losses. Their network model can predict fine-grained gaze in unrestricted environments using face images as input. This method, similar to the majority of those that rely on face images, does not take into account long-distance dependencies between the eyes. Due to the fact that face images contain a substantial amount of redundant information that is unrelated to gaze, these approaches are severely limited in their ability to accurately estimate gaze.