目标聚焦：面向中心视觉视频的注视感知目标检测
Eyes on Target: Gaze-Aware Object Detection in Egocentric Video
AI总结：
本文提出Eyes on Target框架，通过将注视信息注入Vision Transformer的注意力机制，提升中心视觉视频中目标检测的准确性，实验表明其在自研数据集和公开基准上均优于无注视基线。
摘要：
人类注视提供了丰富的监督信号，用于理解复杂视觉环境中视觉注意力。本文提出Eyes on Target，一种新颖的深度感知和注视引导的目标检测框架，专为中心视觉视频设计。我们的方法将注视派生的特征注入Vision Transformer（ViT）的注意力机制，有效使空间特征选择偏向人类关注的区域。与传统目标检测器不同，我们的方法强调观众优先的区域以提高目标检测性能。我们在一个中心视觉模拟数据集上验证了方法，该数据集中的人类视觉注意力对任务评估至关重要，展示了其在评估模拟场景中人类表现的潜力。通过广泛的实验和消融研究，我们评估了集成注视模型的有效性，证明了在自研数据集和公开基准（包括Ego4D Ego-Motion和Ego-CH-Gaze数据集）上，相对于无注视基线，检测准确性有持续提升。为了解释模型行为，我们还引入了注视感知的注意力头重要性度量，揭示了注视线索如何调节Transformer注意力动态。
英文摘要：
Human gaze offers rich supervisory signals for understanding visual attention in complex visual environments. In this paper, we propose Eyes on Target, a novel depth-aware and gaze-guided object detection framework designed for egocentric videos. Our approach injects gaze-derived features into the attention mechanism of a Vision Transformer (ViT), effectively biasing spatial feature selection toward human-attended regions. Unlike traditional object detectors that treat all regions equally, our method emphasises viewer-prioritised areas to enhance object detection. We validate our method on an egocentric simulator dataset where human visual attention is critical for task assessment, illustrating its potential in evaluating human performance in simulation scenarios. We evaluate the effectiveness of our gaze-integrated model through extensive experiments and ablation studies, demonstrating consistent gains in detection accuracy over gaze-agnostic baselines on both the custom simulator dataset and public benchmarks, including Ego4D Ego-Motion and Ego-CH-Gaze datasets. To interpret model behaviour, we also introduce a gaze-aware attention head importance metric, revealing how gaze cues modulate transformer attention dynamics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
