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.
Bridging human evaluation to infrared and visible image fusion.arXiv preprint arXiv:2603.03871
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
A 1D token interface with Selective Token Editing improves multimodal image fusion by modeling global appearance factors separately from local 2D structures, yielding best overall performance on four benchmarks.
DRFusion uses Stabilized History Guidance, Soft Temporal Anchoring, and Decoupled Structure-Motion Adaptation to achieve drift-resilient temporal consistency in infrared-visible video fusion.
SFRF combines uncertainty-aware multi-scale registration with frequency-domain thermal consistency and dual-branch fusion to handle unregistered infrared-visible image pairs.
citing papers explorer
-
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.
-
From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion
A 1D token interface with Selective Token Editing improves multimodal image fusion by modeling global appearance factors separately from local 2D structures, yielding best overall performance on four benchmarks.
-
DRFusion: Drift-Resilient Temporally Consistent Infrared-Visible Video Fusion
DRFusion uses Stabilized History Guidance, Soft Temporal Anchoring, and Decoupled Structure-Motion Adaptation to achieve drift-resilient temporal consistency in infrared-visible video fusion.
-
Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images
SFRF combines uncertainty-aware multi-scale registration with frequency-domain thermal consistency and dual-branch fusion to handle unregistered infrared-visible image pairs.