.png){kind=icon}
.png){kind=icon}
Return
SSMPD: Semi-Supervised Learning for Multispectral Pedestrian Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2025.3645626.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Pedestrian detection is a crucial task in computer vision. Utilizing multispectral knowledge, especially, is essential to effectively detect the pedestrians. Existing multispectral pedestrian detection methods, however, perform only in fully-supervised situations. Although studies on semi-supervised object detection have been conducted, they focus only on single modality environments. Therefore, we propose novel semi-supervised multispectral pedestrian detector (SSMPD) that effectively utilizes multispectral knowledge. Our SSMPD consists of three methods that effectively address the pseudo-labels in the multispectral domain and a novel data selection method. First, we introduce a Pedestrian Appearance-Aware (PAA) weight to consider the quality of the pseudo-label by adjusting the multispectral knowledge transfer from the teacher model to the student model. Second, we propose a Unified Modal-Aware Simultaneous (UMAS) learning to consider the single modality (visible or thermal) and multispectral modalities when learning with the pseudo-label. Finally, we introduce a Similarity-based Contrastive (SC) loss to guide the teacher model in enhancing the quality of pseudo-labels. In addition, we provide diverse data selection for more effective semi-supervised learning. Extensive experimental results on the KAIST and LLVIP datasets demonstrate the effectiveness of our method.
Keywords:
Semi-supervised learning
multispectral pedestrian detection
data selection
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W