盲点对词汇-指称映射重要吗？基于婴儿自我中心视频的计算研究
Do Blind Spots Matter for Word-Referent Mapping? A Computational Study with Infant Egocentric Video
AI总结：
针对婴儿6-9个月时词汇-指称映射学习问题，本研究利用单儿童纵向自我中心数据，提出整合人眼盲点知识的自监督掩码策略，基于掩码自编码器视觉主干与对比学习视频-文本模型，其效果至少与随机掩码相当。
摘要：
通常，儿童在6到9个月大时开始学习第一个词汇，将口语表达与视觉指称对象联系起来。在没有先验知识的情况下，首次遇到的词汇可以有无数种解释：它可能指环境中的任何物体、其组成部分或属性。本研究利用一名儿童经验中的纵向、自我中心且生态有效的数据，提出一种自监督且生物学合理的策略来学习强大的视觉表征。我们基于掩码自编码器（masked autoencoder）的视觉主干网络整合了人眼盲点的知识，以定义一种新颖的掩码策略。这种掩码并重建的方法试图模仿人类大脑填补视野空白的方式。这与标准随机掩码策略形成显著差异，后者从生物学角度难以解释。预训练的编码器被用于基于对比学习的视频-文本模型中，该模型能够获取词汇-指称映射。广泛评估表明，所提出的生物学合理掩码策略在从跨情境和时间扩展的片段中学习词汇-指称映射方面，至少与随机掩码策略同样有效。
英文摘要：
Typically, children start to learn their first words between 6 and 9 months, linking spoken utterances to their visual referents. Without prior knowledge, a word encountered for the first time can be interpreted in countless ways; it might refer to any of the objects in the environment, their components, or attributes. Using longitudinal, egocentric, and ecologically valid data from the experience of one child, in this work, we propose a self-supervised and biologically plausible strategy to learn strong visual representations. Our masked autoencoder-based visual backbone incorporates knowledge about the blind spot in human eyes to define a novel masking strategy. This mask and reconstruct approach attempts to mimic the way the human brain fills the gaps in the eyes' field of view. This represents a significant shift from standard random masking strategies, which are difficult to justify from a biological perspective. The pretrained encoder is utilized in a contrastive learning-based video-text model capable of acquiring word-referent mappings. Extensive evaluation suggests that the proposed biologically plausible masking strategy is at least as effective as random masking for learning word-referent mappings from cross-situational and temporally extended episodes.
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