📡 Signal & Communications

🤖 AAAI2026 · 3 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (8) · 🧪 ICML2026 (2) · 🧠 NeurIPS2025 (5) · 📹 ICCV2025 (3) · 🧪 ICML2025 (3)

Text-Guided Channel Perturbation and Pretrained Knowledge Integration for Unified Multi-Modality Image Fusion

This paper proposes UP-Fusion, a unified multi-modality image fusion framework comprising three modules — Semantic-aware Channel Pruning Module (SCPM), Geometric Affine Modulation (GAM), and CLIP Text-guided Channel Perturbation Module (TCPM) — that employs a single set of weights (trained solely on infrared-visible data) to simultaneously handle both IVIF and medical image fusion tasks, achieving state-of-the-art performance on both.

Balancing Multimodal Domain Generalization via Gradient Modulation and Projection

This paper proposes a Gradient Modulation Projection (GMP) strategy that addresses inter-modality optimization imbalance and inter-task gradient conflicts in multimodal domain generalization (MMDG) through two components: Inter-modality Gradient Decoupled Modulation (IGDM) and Conflict-Adaptive Gradient Projection (CAGP), achieving state-of-the-art performance on multiple benchmarks.

Task Aware Modulation Using Representation Learning for Upscaling of Terrestrial Carbon Fluxes

This paper proposes TAM-RL, a framework that formulates terrestrial carbon flux upscaling as a zero-shot regression transfer learning problem. By combining a BiLSTM task encoder with FiLM modulation and a knowledge-guided loss derived from the carbon balance equation, the method achieves a 9.6% reduction in GPP RMSE and a 43.8% improvement in NEE R² over FLUXCOM-X-BASE across 150+ flux tower sites.