📖 NLP Understanding

📹 ICCV2025 · 1 paper notes

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Balancing Task-Invariant Interaction and Task-Specific Adaptation for Unified Image Fusion

TITA proposes a unified image fusion framework that requires no task identifier at inference. It employs an Interaction-enhanced Pixel Attention (IPA) module to explore task-invariant complementary information extraction, an Operation-based Adaptive Fusion (OAF) module to dynamically adapt to task-specific requirements, and the FAMO strategy to mitigate multi-task gradient conflicts.