Trong-Thang Pham, Anh Nguyen, Hien Van Nguyen, Ngan Le · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28502 · open-access PDF
智能眼镜可以帮助用户了解他们的活动和环境,但小型视觉语言模型必须在有限的计算中保留有用的证据。这是困难的,因为多余的框架消耗视觉预算,而侵略的空间压缩可以抛弃相关细节。尽管视线识别了使用者的焦点,但问题可能需要来自周围场景的证据。 为了解决这些限制,我们建议GazeLens,该框架选择与视线和问题引导的代币减少相结合。
Machine translation (Meta M2M100); the English original follows.
Smart glasses can help users understand their activities and surroundings, but small vision-language models must preserve useful evidence within limited computation. This is difficult because redundant frames consume the visual budget, while aggressive spatial compression can discard relevant details. Although gaze identifies the wearer's focus, the question may require evidence from the surrounding scene. To address these constraints, we propose GazeLens, which combines frame selection with gaze- and question-guided token reduction. First, HOG transitions and image sharpness select frames bef
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