Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning
For computer vision researchers working on gaze estimation, this work provides a more semantically consistent and stable method for predicting gazed objects in complex scenes.
The paper reformulates gaze target estimation as a hierarchical reasoning process using object semantics, achieving state-of-the-art AUC scores of 0.961 on GazeFollow, 0.948 on VideoAttentionTarget, 0.987 on ChildPlay, and 0.977 on GOO-Real with only 7.1M parameters.
Gaze target estimation aims to predict the semantic object an observer fixates upon within an image, a task deeply rooted in the object-oriented nature of human gaze. Observers tend to select a specific semantic entity as the attentional target, rather than responding randomly across arbitrary regions of the image. However, existing methods typically model this task as a direct mapping from global features to gaze heatmaps, essentially treating it as a pixel-level regression problem. This approach fails to explicitly represent the gazed object as a distinct entity, making it difficult to produce stable and semantically consistent predictions in complex scenes. To address this, we propose a two-stage gaze estimation framework guided by object semantics, reformulating gaze target estimation as a hierarchical reasoning process. Our method incorporates object-level representations during feature encoding to align image features with discrete semantic entities, then introduces multi-scale feature fusion and geometric constraints from head pose and gaze direction for fine-grained localization and object-level discrimination. Extensive experiments on GazeFollow, VideoAttentionTarget, ChildPlay, and GOO-Real demonstrate that our method achieves AUC of 0.961, 0.948, 0.987, and 0.977 respectively, delivering strong performance across all benchmarks while maintaining a compact parameter size of 7.1M.