分离头与视线表示，提升眼动检测精度
GazeDETR: Gaze Detection using Disentangled Head and Gaze Representations
- 双解码器分离学习头位置与视线方向
- 在多个数据集上达到当前最佳性能
- 适合需要精准眼动分析的应用场景
眼神交流在日常社交中至关重要，量化该行为有助于人机交互与数字表型分析。现有端到端模型通常使用单一解码器同时定位人头并预测视线（如2D点或热力图），导致头位置与视线预测的表示耦合。本文提出GazeDETR，一种具有两个解耦解码器的新型端到端架构，分别学习独立的表示，并有效利用各子任务的注意力场。具体而言，头检测模块依赖局部信息，而视线解码器融合局部与全局信息。该架构在GazeFollow、VideoAttentionTarget和ChildPlay数据集上均取得当前最优结果，显著优于现有端到端方法。
原文摘要 · Abstract (English)
Gaze communication plays a crucial role in daily social interactions. Quantifying this behavior can help in human-computer interaction and digital phenotyping. While end-to-end models exist for gaze target detection, they only utilize a single decoder to simultaneously localize human heads and predict their corresponding gaze (e.g., 2D points or heatmap) in a scene. This multitask learning approach generates a unified and entangled representation for human head localization and gaze location prediction. Herein, we propose GazeDETR, a novel end-to-end architecture with two disentangled decoders that individually learn unique representations and effectively utilize coherent attentive fields for each subtask. More specifically, we demonstrate that its human head predictor utilizes local information, while its gaze decoder incorporates both local and global information. Our proposed architecture achieves state-of-the-art results on the GazeFollow, VideoAttentionTarget and ChildPlay datasets. It outperforms existing end-to-end models with a notable margin.
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