SPECTRA-Gaze: Gaze Target Estimation via Head-Conditioned Spectral Modulation and Reliability-Aware Heatmap Refinement

Gaze target estimation combines head cues and scene information to determine where a person is looking. When a scene contains several plausible targets, the same head pose may not distinguish between them, and distractors may receive strong heatmap responses. Existing methods mainly improve head–scene interaction in the spatial domain, but do not explicitly address ambiguity both before and after heatmap decoding. We view gaze ambiguity as two linked problems: weak target evidence before decoding and unreliable candidate peaks afterward. We propose SPECTRA-Gaze to address both through Head-Conditioned Spectral State Interaction (HSSI) and Reliability-Constrained Nonlocal State Interaction (RCNSI). When nearby plausible targets are difficult to distinguish from head pose alone, scene cues around them become important. The DCT separately represents broad and fine spatial variation in scene features. HSSI forms adaptive frequency bands from scene and head descriptors, allowing head cues to modulate scene details that may help distinguish nearby plausible targets. Interactions among band descriptors then guide spectral processing before decoding. RCNSI uses candidate reliability to weight candidate features alongside pooled scene context and refine the coarse heatmap. A spatial gate combines local heatmap dispersion with reliable candidate support to control the location and magnitude of logit corrections. Both modules use a partially shared dual-stream encoder to reduce parameter redundancy. Extensive experiments on the GazeFollow and VideoAttentionTarget datasets demonstrate that our method consistently outperforms state-of-the-art methods while using 18.05% fewer parameters.