FuScore uses MLLMs to output continuous quality scores for IVIF images, constructs per-image soft labels from four sub-dimensions, and applies a tripartite objective with Thurstone fidelity to achieve higher correlation with human preferences than prior metrics.
LoRA: Low-rank adaptation of large language models
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Mantis is the first Mamba-native PEFT framework for 3D point cloud models, using state-aware adapters and dual-serialization distillation to match performance with only 5% trainable parameters.
Pretrained scalp-EEG foundation models can be transferred to ECoG via adapters and fine-tuning to match or exceed subject-specific baselines on regression tasks while requiring far less per-patient data.
AI systems lack verifiability, versioning, observability, and traceability in their software supply chains, shown by dependency analysis of 48 projects yielding 4,664 direct and 11,508 transitive dependencies totaling 392M lines of code.
citing papers explorer
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Bringing Multimodal Large Language Models to Infrared-Visible Image Fusion Quality Assessment
FuScore uses MLLMs to output continuous quality scores for IVIF images, constructs per-image soft labels from four sub-dimensions, and applies a tripartite objective with Thurstone fidelity to achieve higher correlation with human preferences than prior metrics.
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Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
Mantis is the first Mamba-native PEFT framework for 3D point cloud models, using state-aware adapters and dual-serialization distillation to match performance with only 5% trainable parameters.
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CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings
Pretrained scalp-EEG foundation models can be transferred to ECoG via adapters and fine-tuning to match or exceed subject-specific baselines on regression tasks while requiring far less per-patient data.
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The Grand Software Supply Chain of AI Systems
AI systems lack verifiability, versioning, observability, and traceability in their software supply chains, shown by dependency analysis of 48 projects yielding 4,664 direct and 11,508 transitive dependencies totaling 392M lines of code.