Papers › 𝐂²Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection

𝐂²Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection

28 Jun 2023arXiv:2306.16175archive 2025-07-28

Maoxun Yuan, Xingxing Wei

Object detection on visible (RGB) and infrared (IR) images, as an emerging solution to facilitate robust detection for around-the-clock applications, has received extensive attention in recent years. With the help of IR images, object detectors have been more reliable and robust in practical applications by using RGB-IR combined information. However, existing methods still suffer from modality miscalibration and fusion imprecision problems. Since transformer has the powerful capability to model the pairwise correlations between different features, in this paper, we propose a novel Calibrated and Complementary Transformer called C²Former to address these two problems simultaneously. In C²Former, we design an Inter-modality Cross-Attention (ICA) module to obtain the calibrated and complementary features by learning the cross-attention relationship between the RGB and IR modality. To reduce the computational cost caused by computing the global attention in ICA, an Adaptive Feature Sampling (AFS) module is introduced to decrease the dimension of feature maps. Because C²Former performs in the feature domain, it can be embedded into existed RGB-IR object detectors via the backbone network. Thus, one single-stage and one two-stage object detector both incorporating our C²Former are constructed to evaluate its effectiveness and versatility. With extensive experiments on the DroneVehicle and KAIST RGB-IR datasets, we verify that our method can fully utilize the RGB-IR complementary information and achieve robust detection results. The code is available at https://github.com/yuanmaoxun/Calibrated-and-Complementary-Transformer-for-RGB-Infrared-Object-Detection.git.

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Code

yuanmaoxun/c2former officialmentioned in paperpytorch report

yuanmaoxun/calibrated-and-complementary-transformer-for-rgb-infrared-object-detection officialmentioned in paperpytorch report

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Tasks

2D Object Detection Multispectral Object Detection Object Object Detection object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
2D Object Detection	DroneVehicle	C2Former	Val/mAP50	74.2	#3 of 11	Archive leaderboard	report
2D Object Detection	DroneVehicle	C2Former	test/mAP	42.8	#3 of 11	Archive leaderboard	report
2D Object Detection	DroneVehicle	C2Former	test/mAP50	72.8	#3 of 11	Archive leaderboard	report
Multispectral Object Detection	KAIST Multispectral Pedestrian Detection Benchmark	C2former	All Miss Rate	28.39	#4 of 17	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout ICA Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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