Cross-modality images that integrate visible-infrared spectra cues can
provide richer complementary information for object detection. Despite this,
existing visible-infrared object detection methods severely degrade in severe
weather conditions. This failure stems from the pronounced sensitivity of
visible images to environmental perturbations, such as rain, haze, and snow,
which frequently cause false negatives and false positives in detection. To
address this issue, we introduce a novel and challenging task, termed
visible-infrared object detection under adverse weather conditions. To foster
this task, we have constructed a new Severe Weather Visible-Infrared Dataset
(SWVID) with diverse severe weather scenes. Furthermore, we introduce the
Cross-modality Fusion Mamba with Weather-removal (CFMW) to augment detection
accuracy in adverse weather conditions. Thanks to the proposed Weather Removal
Diffusion Model (WRDM) and Cross-modality Fusion Mamba (CFM) modules, CFMW is
able to mine more essential information of pedestrian features in
cross-modality fusion, thus could transfer to other rarer scenarios with high
efficiency and has adequate availability on those platforms with low computing
power. To the best of our knowledge, this is the first study that targeted
improvement and integrated both Diffusion and Mamba modules in cross-modality
object detection, successfully expanding the practical application of this type
of model with its higher accuracy and more advanced architecture. Extensive
experiments on both well-recognized and self-created datasets conclusively
demonstrate that our CFMW achieves state-of-the-art detection performance,
surpassing existing benchmarks. The dataset and source code will be made
publicly available at this https URL

我们介绍了一种在恶劣天气条件下的可见 - 红外目标检测，通过引入 Cross-modality Fusion Mamba with Weather-removal (CFMW) 方法和 Weather Removal Diffusion Model (WRDM)，成功提高了检测性能，超过了现有的基准模型。

CFMW: 跨模态融合蟒蛇用于恶劣天气条件下的多光谱物体检测

CFMW: Cross-modality Fusion Mamba for Multispectral Object Detection  under Adverse Weather Conditions

3D object detection plays an important role in autonomous driving and other
robotics applications. However, these detectors usually require training on
large amounts of annotated data that is expensive and time-consuming to
collect. Instead, we propose leveraging large amounts of unlabeled point cloud
videos by semi-supervised learning of 3D object detectors via temporal graph
neural networks. Our insight is that temporal smoothing can create more
accurate detection results on unlabeled data, and these smoothed detections can
then be used to retrain the detector. We learn to perform this temporal
reasoning with a graph neural network, where edges represent the relationship
between candidate detections in different time frames. After semi-supervised
learning, our method achieves state-of-the-art detection performance on the
challenging nuScenes and H3D benchmarks, compared to baselines trained on the
same amount of labeled data. Project and code are released at
this https URL.

通过时态图神经网络的半监督学习，利用大量未标注的点云视频进行 3D 物体探测，实现了比基线和同等标记数据下的最新检测性能更好的检测，关键词：3D 物体探测；半监督学习；时态图神经网络；未标注数据；最新检测性能。