English(EN) Semantic Hardness Is Not Visual Hardness: Sign-Aware Hard Negative Mining for Sign Language Retrieval

新方法通过关注视觉混淆性提高手语检索效果 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-10 10:26

研究人员开发了一种名为感知感知负例挖掘（SAN）的新方法，以改进手语检索系统。该方法侧重于识别视觉上相似但语义上不同的手语作为负例，解决了当前检索模型的一个关键限制。在 PHOENIX-2014T 数据集上的实验表明，SAN 在保持整体性能的同时，显著提高了细粒度检索的准确性。 AI

影响通过更好地区分视觉上相似的手语，提高了手语检索系统的准确性。

排序理由该集群包含一篇详细介绍手语检索新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 该集群包含一篇详细介绍手语检索新方法的学术论文。
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arXiv cs.CV TIER_1 English(EN) · Junmyeong Lee, Chan Hur, ChangSu Choi, Sukmin Cho, Fitsum Gaim, Eui Jun Hwang, Hoyun Song, KyungTae Lim · 2026-07-13 04:00

语义难度不等于视觉难度：面向手语检索的感知一致性负样本挖掘

arXiv:2607.09263v1 Announce Type: new Abstract: Sign Language Retrieval (SLRet) enables efficient access to sign language content but remains fragile in fine-grained scenarios where visually similar signs must be distinguished. We show that this limitation does not stem from mode…
arXiv cs.CV TIER_1 English(EN) · KyungTae Lim · 2026-07-10 10:26

语义难度不等于视觉难度：面向手语检索的感知一致性负样本挖掘

Sign Language Retrieval (SLRet) enables efficient access to sign language content but remains fragile in fine-grained scenarios where visually similar signs must be distinguished. We show that this limitation does not stem from model capacity, but from ineffective hard negative s…