CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.
Chain-of-thought compression should not be blind: V-skip for efficient multimodal reasoning via dual-path anchoring
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
A singing voice conversion system with boundary-aware information bottleneck and high-frequency augmentation achieves the best naturalness in SVCC2025 subjective tests while using less extra data than competitors.
citing papers explorer
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CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
CFMS is a coarse-to-fine framework that uses MLLMs to create a multi-perspective knowledge tuple as a reasoning map for symbolic table operations, yielding competitive accuracy on WikiTQ and TabFact.
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Sparsity-Aware Voxel Attention and Foreground Modulation for 3D Semantic Scene Completion
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
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Controllable Singing Style Conversion with Boundary-Aware Information Bottleneck
A singing voice conversion system with boundary-aware information bottleneck and high-frequency augmentation achieves the best naturalness in SVCC2025 subjective tests while using less extra data than competitors.