Object detection in visible (RGB) and infrared (IR) images has been widely
applied in recent years. Leveraging the complementary characteristics of RGB
and IR images, the object detector provides reliable and robust object
localization from day to night. Existing fusion strategies directly inject RGB
and IR images into convolution neural networks, leading to inferior detection
performance. Since the RGB and IR features have modality-specific noise, these
strategies will worsen the fused features along with the propagation. Inspired
by the mechanism of human brain processing multimodal information, this work
introduces a new coarse-to-fine perspective to purify and fuse two modality
features. Specifically, following this perspective, we design a Redundant
Spectrum Removal module to coarsely remove interfering information within each
modality and a Dynamic Feature Selection module to finely select the desired
features for feature fusion. To verify the effectiveness of the coarse-to-fine
fusion strategy, we construct a new object detector called Removal and
Selection Detector (RSDet). Extensive experiments on three RGB-IR object
detection datasets verify the superior performance of our method.

本文介绍了一种新的粗到细的特征融合策略，利用 RGB 和红外图像的互补特性，通过去除冗余光谱和动态特征选择，提高了目标检测的性能。通过构建名为 RSDet 的新型目标检测器，并在三个 RGB-IR 目标检测数据集上进行广泛实验，验证了我们方法的优越性能。

去除与选择：通过粗糙到细化融合改进 RGB - 红外目标检测

Removal and Selection: Improving RGB-Infrared Object Detection via  Coarse-to-Fine Fusion

We present OSFormer, the first one-stage transformer framework for
camouflaged instance segmentation (CIS). OSFormer is based on two key designs.
First, we design a location-sensing transformer (LST) to obtain the location
label and instance-aware parameters by introducing the location-guided queries
and the blend-convolution feedforward network. Second, we develop a
coarse-to-fine fusion (CFF) to merge diverse context information from the LST
encoder and CNN backbone. Coupling these two components enables OSFormer to
efficiently blend local features and long-range context dependencies for
predicting camouflaged instances. Compared with two-stage frameworks, our
OSFormer reaches 41% AP and achieves good convergence efficiency without
requiring enormous training data, i.e., only 3,040 samples under 60 epochs.
Code link: this https URL

OSFormer 是第一个使用一种阶段变压器框架来进行伪装实例分割（CIS）的研究，包括关键设计的位置感知变压器（LST）和粗到细的融合（CFF）来融合来自 LST 编码器和 CNN 主干的多样化上下文信息，在不需要大量训练数据（仅 60 个时期下的 3,040 个样本）的情况下，OSFormer 达到了 41％AP，并实现了良好的收敛效率。