Search Results for author:
Found 5 papers, 2 papers with code
UniRGB-IR: A Unified Framework for Visible-Infrared Downstream Tasks via Adapter Tuning
Semantic analysis on visible (RGB) and infrared (IR) images has gained attention for its ability to be more accurate and robust under low-illumination and complex weather conditions.
Removal and Selection: Improving RGB-Infrared Object Detection via Coarse-to-Fine Fusion
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.
Learning to Pan-sharpening with Memories of Spatial Details
To address this issue, in this paper we observe that the spatial details from PAN images are mainly high-frequency cues, i. e., the edges reflect the contour of input PAN images.
$\mathbf{C}^2$Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
In $\mathrm{C}^2$Former, 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.
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
Then, we propose a Translation-Scale-Rotation Alignment (TSRA) module to address the problem by calibrating the feature maps from these two modalities.
