Multispectral Object Detection
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Benchmarks
Most implemented
Fully Convolutional Networks for Semantic Segmentation
$\mathbf{C}^2$Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems
Multispectral Deep Neural Networks for Pedestrian Detection
YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework
Multispectral Detection Transformer with Infrared-Centric Sensor Fusion
Papers
CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection
RGB--T object detection exploits the complementary strengths of visible and infrared imagery, supporting robust perception in low-light, adverse-weather, and complex multi-scale environments. However, existing methods st…
Multispectral Object DetectionComputational EfficiencyFully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection
Existing multispectral detectors are limited by discrete spectral processing, a scale-dependent shift in the relative reliability of spectral and spatial cues across pyramid levels, and the lack of explicit rotation-equi…
Multispectral Object DetectionDual Sparse Aggregation Transformer for Multispectral Object Detection
Transformer-based approaches have obtained excellent performance in multispectral object detection tasks due to their ability to model long-range dependencies and capture complementary information. However, previous tran…
Multispectral Object DetectionProgressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection
Effective cross-modal feature alignment and interaction are central challenges in multispectral object detection. Although global cross-attention provides strong long-range modeling ability, its quadratic complexity with…
Multispectral Object DetectionSemantic correspondenceLong-range modelingSpectraDINO: Modality-Conditioned Adaptation of RGB Vision Foundation Models Across Infrared Bands
Vision foundation models (VFMs) pretrained on large-scale RGB data provide strong general-purpose representations, yet infrared perception, which is essential for robotics and driving in low light and adverse weather, st…
Multispectral Object DetectionSemantic SegmentationBridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection
Text-guided multispectral object detection uses text semantics to guide semantic-aware cross-modal interaction between RGB and IR for more robust perception. However, notable limitations remain: (1) existing methods ofte…
Multispectral Object Detection