| 10 |
Depth AnyEvent: A Cross-Modal Distillation Paradigm for Event-Based Monocular Depth Estimation |
提出基于跨模态蒸馏的事件相机单目深度估计方法 |
distillation depth estimation monocular depth |
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| 11 |
Efficient Multimodal Dataset Distillation via Generative Models |
提出EDGE:一种基于生成模型的高效多模态数据集蒸馏方法 |
distillation large language model multimodal |
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| 12 |
Comparing Computational Pathology Foundation Models using Representational Similarity Analysis |
利用表征相似性分析比较计算病理学中的多个预训练模型 |
contrastive learning distillation foundation model |
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| 13 |
Self-supervised learning of imaging and clinical signatures using a multimodal joint-embedding predictive architecture |
利用多模态联合嵌入预测架构的自监督学习提升肺结节诊断 |
predictive model multimodal |
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| 14 |
NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training |
NeuroRAD-FM:基于分布鲁棒训练的神经肿瘤学Foundation Model |
MAE foundation model |
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| 15 |
Beyond Random Masking: A Dual-Stream Approach for Rotation-Invariant Point Cloud Masked Autoencoders |
提出双流掩码自编码器,解决点云旋转不变性学习中几何结构和语义一致性缺失问题 |
masked autoencoder MAE curriculum learning |
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| 16 |
Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception |
提出AdaptiveNN,通过模仿人类自适应视觉实现高效灵活的机器视觉感知 |
reinforcement learning representation learning embodied AI |
✅ |
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