研究人员开发了 ZEBRA，一个旨在提高音频语言模型 (ALM) 泛化能力的新框架。ZEBRA 解决了提示学习在提高已知类别性能的同时可能降低新类别或未见类别准确性的权衡问题。通过将零样本和提示学习的 logits 与自熵正则化相结合，ZEBRA 旨在减少对基础类别的过拟合，并显著缩小基础到新颖泛化的差距。实验表明，ZEBRA 在提高新类别性能的同时，能够保持强大的基础准确性。 AI
影响 提高了音频 AI 系统的泛化能力，有望在多样化数据集上实现更鲁棒的音频分类和理解。
排序理由 该集群包含两篇详细介绍音频语言模型新研究和方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2606.31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes through few-shot supervised adaptation, we observe a c…
arXiv:2511.16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored. Crucially, there is no consensus on whether audio-language models can build effective general-pur…
Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes through few-shot supervised adaptation, we observe a critical trade-off: it often degrades performance o…