The paper introduces an Attention-Driven Complementarity Resampling framework to enhance cross-modality object detection. It employs a shared channel spatial attention mechanism that exchanges semantic masks between modalities, encouraging the backbone to learn generalized features. Additionally, a learnable channel competition module samples and aggregates features channel‑wise, improving robustness and achieving competitive results on multiple datasets.
By Guandi Wang, Ming Li, Yunsen Xing, Junle Liu
arXiv:2608.06205v2 Announce Type: replace
Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
By Nima Hatami, Karim Faez, Saeed Sharifian, Hamidreza Amindavar
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.
The paper introduces Feature Interaction Network (FINE), a lightweight semantic alignment module for feature fusion networks in object detectors. FINE refines low‑level features using high‑level contextual guidance through cross‑level attention, and employs Alignment‑Aware Token Sampling to reduce attention complexity. The resulting spatial‑channel modulation map selectively enhances semantically relevant pixels while preserving sub‑pixel localization, leading to improved detection accuracy with minimal computational overhead.
By Hyungseop Lee, Jiho Lee, Woochul Kang
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
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.
By Hao Zhang, Xuhui Zuo, Jiayi Ma
Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptimal recognition performance. Current approaches ex...
arXiv:2607. 16338v1 Announce Type: cross Abstract: This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification.
By Anamitra Ghosh, Abhiroop Chatterjee, Susmita Ghosh
arXiv:2608.20944v1 Announce Type: new
Abstract: Multimodal object detection in remote sensing faces challenges due to semantic heterogeneity and modality-specific noise interference. To this end, we...
By Xin Wu, Zhenyu Gao, Qiankun Zhang, Shaoyong Guo
Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that adapt to diverse user requirements and downstream tasks. However, existing methods typically rely on predefined discrete control conditions, leading to a sparse space that fails to support fine-grained modulation demands.
arXiv:2607. 08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions.
By Wenhao Dong, Xiaoyan Luo, Linlin Yang, Haodong Zhu, Xiaorong Shi, Guodong Guo, Baochang Zhang
arXiv:2606. 02659v1 Announce Type: cross Abstract: Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making.
By Dong Li, Lingling Zhang, Binghao Han, Linlin Ding, Yue Kou