| 17 |
Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling |
提出Kling-MLLM以解决视频生成中的多模态理解问题 |
reinforcement learning foundation model multimodal |
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| 18 |
YANAMI: A Large-Scale Multimodal Dataset and Benchmark for Multiturn Stateful Interaction |
提出YANAMI以解决多轮状态交互的多模态数据集不足问题 |
world model world models multimodal |
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| 19 |
MambaHand: Local Identity and Behavioral Sequence Learning with Mamba for Dynamic Hand Gesture Authentication |
提出MambaHand以解决动态手势认证中的身份与行为序列学习问题 |
Mamba representation learning motion representation |
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| 20 |
Mamba3R: Fixed-Capacity Selective State Memory for Long-Sequence 3D Reconstruction |
提出Mamba3R以解决长序列3D重建的内存限制问题 |
Mamba 3D reconstruction |
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| 21 |
StepX-OPD: Rethinking Multi-Teacher On-Policy Distillation in Diffusion Models |
提出StepX-OPD以解决多教师策略蒸馏在扩散模型中的效率问题 |
distillation instruction following |
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| 22 |
DreamX: Video Diffusion Model is a Universal Latent Action Learner |
提出DreamX以解决视频动作表示学习中的转移问题 |
policy learning cross-embodiment spatiotemporal |
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| 23 |
ReconDreamer#: Self-Evolving Closed-Loop Synergy of Reconstruction and Generation for Dynamic Driving Scenes |
提出ReconDreamer#以解决动态驾驶场景重建与生成的协同问题 |
dreamer scene reconstruction |
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| 24 |
Multi-Path Mamba Diffusion for 3D Point Cloud Semantic Scene Completion |
提出MPath Mamba扩散网络以解决3D点云语义场景补全问题 |
Mamba state space model |
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| 25 |
E2I-Flow: EEG-to-Image Reconstruction via Wavelet-Temporal Cross-Attention and Conditional Flow Matching |
提出E2I-Flow以解决EEG信号到图像重建问题 |
flow matching representation learning |
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| 26 |
Polarimetric Scattering-Structure Contrastive Learning for Few-Shot HRRP Recognition |
提出极化散射结构对比学习以解决少样本高分辨率HRRP识别问题 |
contrastive learning |
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| 27 |
Pixel-space video generation with U-JiT |
提出U-JiT以解决像素空间视频生成的挑战 |
flow matching spatiotemporal |
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| 28 |
Cycle WAM: Coupling Action and Video through Cycle Consistency |
提出Cycle WAM以解决视频与动作一致性问题 |
world action model world action models |
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