arXiv AI
A2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration
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The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.
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SuppreSensing: Expert-Guided Feature Recalibration and Discrepancy Augmentation for Multimodal Object Detection
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MATS: A novel multi-modality multi-task learning framework for 3D perception in autonomous driving
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P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation
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arXiv Computer Vision
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Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration
The paper introduces W2F, a visual‑semantic mutual‑reinforcing framework for multi‑modal image fusion (MMIF). It enhances feature complementarity with a segmentation‑guided fusion loss and an embedded information restoration prior, and employs interactive gated mixed attention (IGM‑Att) and progressive coupling attention (PC‑Att) to fuse local and global information. A collaborative mechanism with a feature search module and gradient correction further aligns visual and semantic fusion, leading to state‑of‑the‑art visual appeal and decision accuracy.