Despite the significant progress in image denoising, it is still challenging
to restore fine-scale details while removing noise, especially in extremely
low-light environments. Leveraging near-infrared (NIR) images to assist visible
RGB image denoising shows the potential to address this issue, becoming a
promising technology. Nonetheless, existing works still struggle with taking
advantage of NIR information effectively for real-world image denoising, due to
the content inconsistency between NIR-RGB images and the scarcity of real-world
paired datasets. To alleviate the problem, we propose an efficient Selective
Fusion Module (SFM), which can be plug-and-played into the advanced denoising
networks to merge the deep NIR-RGB features. Specifically, we sequentially
perform the global and local modulation for NIR and RGB features, and then
integrate the two modulated features. Furthermore, we present a Real-world
NIR-Assisted Image Denoising (Real-NAID) dataset, which covers diverse
scenarios as well as various noise levels. Extensive experiments on both
synthetic and our real-world datasets demonstrate that the proposed method
achieves better results than state-of-the-art ones. The dataset, codes, and
pre-trained models will be publicly available at
this https URL

通过有效利用近红外图像辅助可见光图像去噪，提出了一种选择性融合模块（SFM）来解决近红外 - 可见光图像去噪的内容不一致和数据稀缺等问题，并通过综合全局和局部调节的方式实现了两种特征的融合。实验结果表明，该方法在合成和真实数据集上均取得了优于现有方法的效果，同时还提供了公开的数据集、代码和预训练模型。

NIR 辅助图像去噪：一种选择性融合方法与真实世界基准数据集

NIR-Assisted Image Denoising: A Selective Fusion Approach and A  Real-World Benchmark Datase

Achieving real-time and accuracy on embedded platforms has always been the
pursuit of road segmentation methods. To this end, they have proposed many
lightweight networks. However, they ignore the fact that roads are "stuff"
(background or environmental elements) rather than "things" (specific
identifiable objects), which inspires us to explore the feasibility of
representing roads with low-level instead of high-level features. Surprisingly,
we find that the primary stage of mainstream network models is sufficient to
represent most pixels of the road for segmentation. Motivated by this, we
propose a Low-level Feature Dominated Road Segmentation network (LFD-RoadSeg).
Specifically, LFD-RoadSeg employs a bilateral structure. The spatial detail
branch is firstly designed to extract low-level feature representation for the
road by the first stage of ResNet-18. To suppress texture-less regions mistaken
as the road in the low-level feature, the context semantic branch is then
designed to extract the context feature in a fast manner. To this end, in the
second branch, we asymmetrically downsample the input image and design an
aggregation module to achieve comparable receptive fields to the third stage of
ResNet-18 but with less time consumption. Finally, to segment the road from the
low-level feature, a selective fusion module is proposed to calculate
pixel-wise attention between the low-level representation and context feature,
and suppress the non-road low-level response by this attention. On KITTI-Road,
LFD-RoadSeg achieves a maximum F1-measure (MaxF) of 95.21% and an average
precision of 93.71%, while reaching 238 FPS on a single TITAN Xp and 54 FPS on
a Jetson TX2, all with a compact model size of just 936k parameters. The source
code is available at this https URL

通过利用低级特征代替高级特征，本研究提出了一种低级特征主导的道路分割模型（LFD-RoadSeg），采用双边结构，并结合选择性融合模块来实现道路的实时准确分割，模型在 KITTI-Road 数据集上获得了最大 F1-measure（95.21%）和平均精度（93.71%），在单个 TITAN Xp 上实现 238 FPS，在 Jetson TX2 上实现 54 FPS，模型参数仅为 936k。

利用低级表示进行超快速道路分割

Exploiting Low-level Representations for Ultra-Fast Road Segmentation

The local and global features are both essential for automatic speech
recognition (ASR). Many recent methods have verified that simply combining
local and global features can further promote ASR performance. However, these
methods pay less attention to the interaction of local and global features, and
their series architectures are rigid to reflect local and global relationships.
To address these issues, this paper proposes InterFormer for interactive local
and global features fusion to learn a better representation for ASR.
Specifically, we combine the convolution block with the transformer block in a
parallel design. Besides, we propose a bidirectional feature interaction module
(BFIM) and a selective fusion module (SFM) to implement the interaction and
fusion of local and global features, respectively. Extensive experiments on
public ASR datasets demonstrate the effectiveness of our proposed InterFormer
and its superior performance over the other Transformer and Conformer models.

本文提出 InterFormer 方法，使用双向特征交互模块和选择性融合模块实现局部和全局特征的交互和融合，应用于自动语音识别领域，取得比 Transformer 和 Conformer 更好的性能表现。