物体先于词汇：用于从儿童视角视频中语言接地学习的物体优先归纳偏置
Objects Before Words: Object-First Inductive Biases for Grounding Language in Child-View Video
AI总结：
针对婴儿视角视频中命名参照物出现时间和位置的双重歧义，提出BabyMind方法，通过物体优先的归纳偏置、掩码区域接口和原型空间多实例对比学习，在稀疏弱监督下提升语言接地性能。
摘要：
从自然经验中学习接地词汇含义需要解决婴儿视角记录中的两个歧义：命名参照物何时出现以及在杂乱画面中的位置。在SAYCam风格的数据中，看护者的语言稀疏且与自我中心视频弱同步，因此单帧对比配对会产生噪声正样本，其中目标物体缺失或被干扰物纠缠。我们提出BabyMind，一种在稀疏、噪声监督下用于儿童视角对比学习的物体优先偏置。BabyMind使用离线掩码区域接口提取候选物体嵌入，通过跟踪将短话语中心窗口内的候选物体链接成轻量级物体文件，并使用原型空间多实例对比目标将话语与物体文件袋对齐。轨迹一致性和全局物体一致性正则化器稳定学习，并将物体文件结构转移到评估时使用的全局帧嵌入中。在SAYCam-S上，BabyMind将Labeled-S 15强制选择准确率比CVCL提高了+2.6个点，并在词汇内分布外基准测试中取得一致提升。代码可在该网址获取。
英文摘要：
Learning grounded word meaning from natural experience requires resolving two ambiguities in infant-view recordings: when the named referent appears and where it is in a cluttered frame. In SAYCam-style data, caregiver speech is sparse and weakly synchronized with egocentric video, so single-frame contrastive pairing yields noisy positives in which the intended object is absent or entangled with distractors. We propose BabyMind, an object-first bias for child-view contrastive learning under sparse, noisy supervision. BabyMind extracts candidate object embeddings using an offline mask-based region interface, links candidates across a short utterance-centered window into lightweight object files via tracking, and aligns utterances to bags of object files with a prototype-space multiple-instance contrastive objective. Track-coherence and global-object agreement regularizers stabilize learning and transfer object-file structure into the global frame embedding used at evaluation. On SAYCam-S, BabyMind improves Labeled-S 15 forced-choice accuracy by +2.6 points over CVCL and yields consistent gains on in-vocabulary out-of-distribution benchmarks. Code is available at https://github.com/sathiiii/BabyMind.
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。
