| 1 |
Visual Grounding First, Multimodal In-context Learning Follows |
提出ProCoRe以解决多模态ICL中的视觉基础问题 |
reinforcement learning multimodal visual grounding |
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| 2 |
I$^2$-KAN: Interpretable Intra- and Inter-Modal Multi-Pathway Representation Learning for Multimodal Sentiment Analysis |
提出I$^2$-KAN以解决多模态情感分析中的可解释性问题 |
representation learning multimodal |
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| 3 |
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success |
提出传感运动对齐方法以评估机器人模型能力 |
world action model world action models vision-language-action |
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| 4 |
HiSTEVE 1.0: Harnessing Human-Interface VLA Agents for Open-World Games |
提出HiSTEVE以解决开放世界游戏中的自主控制问题 |
reinforcement learning imitation learning VLA |
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| 5 |
Learning from Next-Frame Prediction: Towards Unified and Scalable Visual Representation Learning |
提出VANE框架以解决视频预测与视觉表示学习的统一性问题 |
world model world models JEPA |
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| 6 |
GM-GReFEL: Geometry-Guided Spatiotemporal State-Space Learning for Robust Dynamic Facial Expression Recognition |
提出GM-GReFEL以解决动态人脸表情识别中的噪声标签和计算复杂度问题 |
Mamba spatiotemporal |
✅ |
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| 7 |
Where Do Long Captions Fail? Position-Aware Diagnosis and Reinforcement Learning for Detailed Image Captioning |
提出位置感知诊断与强化学习以解决长文本图像描述问题 |
reinforcement learning visual grounding |
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| 8 |
WovenAnchor Matcher: Specialized Intra- and Inter-Image Context Modeling for Feature Matching |
提出WovenAnchor Matcher以解决特征匹配中的上下文建模问题 |
Mamba feature matching |
✅ |
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| 9 |
Reading, Writing, Sharing, and Interacting with the World through Video |
通过视频基础模型实现世界的阅读、写作与交互 |
world model world models foundation model |
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| 10 |
RECAP: Looking Once Is Not Enough for Vision-Language Reasoning |
提出RECAP以解决视觉语言推理中的时间基础失效问题 |
reinforcement learning chain-of-thought |
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| 11 |
One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement |
提出SKD-ETR框架以解决FUDA中的标签噪声与通信效率问题 |
distillation |
✅ |
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| 12 |
CODI: Improving Self-Supervised Vision Transformers with Cross Distillation |
提出CODI以改善自监督视觉变换器的密集预测能力 |
distillation |
✅ |
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| 13 |
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success |
提出传感运动对齐方法以评估机器人模型能力 |
world action model world action models vision-language-action |
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| 14 |
Learning from Next-Frame Prediction: Towards Unified and Scalable Visual World Modeling |
提出VANE框架以解决视频世界建模中的未来预测问题 |
world model world models JEPA |
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| 15 |
PixGR: Pixel-Space Generative Decoding for Fast Feed-Forward 3D Reconstruction |
提出PixGR以解决快速前馈3D重建中的场景不确定性问题 |
flow matching 3D reconstruction |
✅ |
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| 16 |
StackGen3D: Stable 3D Block Stack Generation from Multiview Silhouettes |
提出StackGen3D以解决稳定3D块堆叠生成问题 |
flow matching penetration multimodal |
✅ |
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| 17 |
VisFlow: Interpretable Flow Matching for Visibility Reconstruction, Characterization, and Recognition |
提出VisFlow以解决可见性重建与表征问题 |
flow matching representation learning |
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| 18 |
Interpreting Physics in Video Encoders using Multi-Object Interaction Videos |
提出多物体交互视频解析物理属性的方法以提升视频编码器的可解释性 |
world model world models |
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