Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges with convolutional neural network (CNN)-based, transformer-based, and contrastive language-image pre-training (CLIP)-based methods, including late fusion of image features, lack of factor-aware conditioning, and impractical capacity scaling. To address these challenges, we propose Globally-conditioned Multi-scale Gaze estimation (GMGaze), which leverages a multi-scale transformer architecture. Specifically, the model first introduces semantic prototype conditioning, which modulates the CLIP global image embedding using four learned prototype banks (i.e., illumination, background, head pose and appearance) to generate two complementary context-biased global tokens. These tokens, along with the CLIP patch and CNN tokens, are fused at the first layer. This early unified fusion prevents information loss common in late-stage merging. Finally, each token passes through sparse Mixture-of-Experts modules, providing conditional computational capacity without uniformly increasing dense parameters. For cross-domain adaptation, we incorporate an adversarial domain adaptation technique with a feature separation loss that encourages the two global tokens to remain de-correlated. Experiments using four public benchmarks (MPIIFaceGaze, EYEDIAP, Gaze360, and ETH-XGaze) show that GMGaze achieves mean angular errors of 2.49$^\circ$, 3.22$^\circ$, 10.16$^\circ$, and 1.44$^\circ$, respectively, outperforming previous baselines in all within-domain settings. In cross-domain evaluations, it provides state-of-the-art (SOTA) results on two standard transfer routes.

翻译：注视估计方法通常利用面部外观预测人的注视方向。然而，先前研究表明，基于卷积神经网络（CNN）、Transformer和对比语言-图像预训练（CLIP）的方法存在三大挑战：图像特征融合延迟、缺乏因子感知条件控制以及容量扩展不实用。为解决这些问题，我们提出全局条件多尺度注视估计方法（GMGaze），其采用多尺度Transformer架构。具体而言，模型首先引入语义原型条件控制，通过四个学习到的原型库（即光照、背景、头部姿态和外观）调制CLIP全局图像嵌入，生成两类互补的上下文偏置全局标记。这些标记与CLIP补丁标记及CNN标记在第一层完成融合。这种早期统一融合避免了后期合并中常见的信息丢失。最后，每个标记通过稀疏混合专家模块，在无需均匀增加密集参数的情况下提供条件计算容量。针对跨域适应，我们引入对抗域适应技术，并采用特征分离损失促进两个全局标记保持去相关。在四个公开基准数据集（MPIIFaceGaze、EYEDIAP、Gaze360和ETH-XGaze）上的实验表明，GMGaze分别达到2.49°、3.22°、10.16°和1.44°的平均角度误差，在所有域内设置中均优于先前基线方法。在跨域评估中，该方法在两个标准迁移路径上取得了最先进（SOTA）结果。