arXiv:2607. 08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2608.06205v2 Announce Type: replace Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
RoES is a Rotational Equivariant Selective-frequency fusion network that dynamically separates low- and high-frequency components of infrared-visible images. It uses a trainable rotation-enhanced updater to decouple frequencies, then fuses them with a dual-branch module: a rotation-equivariant Mamba for low-frequency structural dependencies and a polar spectral attention Dual-Fourier block for high-frequency detail refinement. Experiments show RoES outperforms existing methods in fusion quality and downstream object detection, offering a robust multimodal fusion solution.
arXiv:2608. 02092v2 Announce Type: replace Abstract: Deep multimodal fusion for object detection has demonstrated good performance through mining modal characteristics.
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...
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.
RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.