Tag: Multimodal Data
All the articles with the tag "Multimodal Data".
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Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
This paper proposes a novel method combining contrastive learning with conditional variational autoencoders and mutual information constraints to extract style features from unlabeled data, demonstrating effectiveness on simple datasets like MNIST while facing challenges with natural image datasets due to augmentation limitations and qualitative evaluation.
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Investigating Task Arithmetic for Zero-Shot Information Retrieval
本文提出任务算术方法,通过参数加减操作实现零样本信息检索的领域和语言适应,在科学、生物医学和多语言数据集上取得最高18%的NDCG@10提升,展现了轻量级模型适应的潜力。
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Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
本文通过提出AI记忆系统的分类(参数、上下文结构化和非结构化)和六种基本操作(整合、更新、索引、遗忘、检索、压缩),系统化地综述了长期记忆、长上下文、参数修改和多源记忆等研究主题,并展望了未来方向。
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MOOSComp: Improving Lightweight Long-Context Compressor via Mitigating Over-Smoothing and Incorporating Outlier Scores
本文提出MOOSComp方法,通过在训练中添加inter-class cosine similarity loss缓解over-smoothing问题,并在压缩中整合outlier分数保留关键token,显著提升了任务无关的长上下文压缩性能和泛化能力。
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R&B: Domain Regrouping and Data Mixture Balancing for Efficient Foundation Model Training
R&B框架通过基于语义相似性的数据重新分组和梯度驱动的动态权重调整,以极低的计算开销(0.01%)在自然语言和多模态任务中匹配或超越现有数据混合策略,提升了基础模型训练效率。