Papers Multispectral Object Detection
“Multispectral Object Detection” 태그가 달린 논문 52편 · 필터 해제
MLPD: Multi-Label Pedestrian Detector in Multispectral Domain
Multispectral pedestrian detection has been actively studied as a promising multi-modality solution to handle illumination and weather changes. Most multi-modality approaches carry the assumption that all inputs are full…
Multi-Label LearningMultispectral Object DetectionPedestrian DetectionMultispectral Object Detection with Deep Learning
Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as t…
Data AugmentationDeep LearningMultispectral Object DetectionObject+2Guided Attentive Feature Fusion for Multispectral Pedestrian Detection
Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable. To benefit from both RGB and thermal IR modalities, we introduce a novel attentive …
Multispectral Object Detectionobject-detectionObject DetectionPedestrian DetectionMultispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, …
Multispectral Object DetectionObjectobject-detectionObject DetectionImproving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems
Multispectral pedestrian detection is capable of adapting to insufficient illumination conditions by leveraging color-thermal modalities. On the other hand, it is still lacking of in-depth insights on how to fuse the two…
Computational EfficiencyMultispectral Object DetectionPedestrian DetectionWeakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection
Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffer…
2D Object DetectionMultispectral Object DetectionPositionCIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate…
2D Object DetectionMultispectral Object DetectionSegmentationSemantic Segmentation+3Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
Multispectral pedestrian detection has attracted increasing attention from the research community due to its crucial competence for many around-the-clock applications (e.g., video surveillance and autonomous driving), es…
Autonomous DrivingMultispectral Object DetectionPedestrian DetectionSemantic SegmentationIllumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies …
Multispectral Object DetectionPedestrian DetectionFusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and au…
Autonomous DrivingMultispectral Object DetectionMulti-Task LearningPedestrian Detection+1Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it i…
2D Object Detection3D Object DetectionMultispectral Object DetectionObject Detection+1Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segme…
Crack SegmentationMultispectral Object DetectionMulti-tissue Nucleus SegmentationSegmentation+2