The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
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FaVChat proposes hierarchical prompt-query guided visual features and Data-Efficient GRPO for efficient training, plus the FaVChat-170K dataset, claiming consistent outperformance over prior VLLMs on facial video tasks.
A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.
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Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
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FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO
FaVChat proposes hierarchical prompt-query guided visual features and Data-Efficient GRPO for efficient training, plus the FaVChat-170K dataset, claiming consistent outperformance over prior VLLMs on facial video tasks.
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Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising
A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.