提出一种简单域泛化方法，显著增强现代视觉语言模型下像素级图像篡改检测的鲁棒性。
arXiv:2607.18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabi…
提出“净化-引导”新策略，解决多模态人脸防伪的领域泛化难题，兼顾模态偏差与域偏移。
arXiv:2505.09484v2 Announce Type: replace Abstract: Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse sc…
不用重新训练，仅靠单源数据！新方法用生理结构化的频谱建模实现跨域睡眠分期泛化。
arXiv:2607.04851v1 Announce Type: cross Abstract: Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalizati…
内核视角揭示域泛化与上下文学习的隐秘关联，为高效Transformer提供新思路。
arXiv:2607.00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different …
用对抗提示调优与生成破解单域泛化难题，CVPR 2025新思路不容错过。
arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unse…
强化学习新框架“Connect the Dots”让LLM代理实现跨域泛化与长期自学习，迈向更智能的自主决策。
arXiv:2606.20002v1 Announce Type: cross Abstract: This work presents a general framework for training large language models (LLMs) to "Connect the Dot…
反因果视角突破域泛化瓶颈，巧用未标注数据提升模型泛化能力，ICML 2026前沿研究
arXiv:2602.17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to …
评估合成数据在胎儿脑MRI分割域泛化中的效果，探索提升模型跨域鲁棒性的新思路。
arXiv:2411.06842v3 Announce Type: replace-cross Abstract: Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studyin…
用小语言模型低成本搞定生物医学声明验证，揭秘结构化数据集捷径与跨域泛化新发现。
arXiv:2606.12854v1 Announce Type: new Abstract: Large Language Models such as GPT-4o and GPT-5 achieve strong zero-shot performance on biomedical clai…
新方法RT-SDGOD专攻实时目标检测在天气、成像变化下的分布偏移难题，从问题定义层面提升泛化能力
arXiv:2606.09367v1 Announce Type: new Abstract: In real-world deployment under strict real-time constraints, weather and imaging variations induce sig…
医疗图像分割中，跨域自监督方法智能挑选代表性样本，显著降低人工标注成本。
arXiv:2606.04301v1 Announce Type: new Abstract: Acquiring labeled medical image data is resource-intensive and a challenge further exacerbated in cros…
首个多模态领域泛化基准，覆盖多种跨模态场景，助力模型稳健性评估。
arXiv:2606.00891v1 Announce Type: new Abstract: Multi-modal Domain Generalization (MMDG) seeks to leverage complementary modalities to enhance model r…
利用未标注目标数据与源域监督，实现稳健的跨域泛化，突破领域差异瓶颈。
arXiv:2605.29122v1 Announce Type: new Abstract: It is often desirable to generalize medical imaging AI models trained with dense annotations to data a…
揭秘视觉基础模型在面部深度伪造检测中的跨域泛化极限，为AI安全提供关键洞察。
arXiv:2605.24965v1 Announce Type: cross Abstract: The rapid evolution of generative models has enabled the creation of hyper-realistic facial deepfake…
利用视觉基础模型（VFM）实现单域泛化目标检测，开创性方法提升跨域适应能力与性能。
arXiv:2604.21502v2 Announce Type: replace Abstract: Real-world weather, illumination, and imaging variations often induce severe domain shifts, degrad…
ICML 2026新方法：用重要性-多样性数据选择提升Web智能体跨域泛化能力
arXiv:2605.20291v1 Announce Type: new Abstract: Large language models (LLMs) have enabled web agents that follow natural language goals through multi-…