Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery

2026-09-16

AI Summary

提出Aligned Consensus Teacher方法，通过区域对齐、跨模态共识和实例增强技术解决弱对齐可见-红外图像中目标检测的标签效率问题。

Abstract

Visible-infrared object detection (VIOD) detects objects with oriented bounding boxes from paired visible and infrared images. Existing methods depend on costly dual-modality annotations. Semi-supervised learning can reduce this burden, but extending it from single-modal detection to VIOD is challenging. In the practical image-pair-level setting considered here, only a few pairs are labeled in both modalities, while the rest are completely unlabeled. This limited supervision creates three challenges: (i) too few labeled boxes for robust cross-modal alignment; (ii) pseudo-label errors caused by branch-wise misses accumulate during self-training; and (iii) tail-class annotations become critically scarce as the labeling budget decreases. We propose Aligned Consensus Teacher (ACT) for label-efficient VIOD in this setting. Its Cycle-Consistent Region Alignment (CRA) combines cycle consistency and sparse anchors with reliability-weighted regional matching. Cross-Modal Consensus Mean-Teacher (CMC-MT) forms consensus pseudo labels under pair-preserving views to recover branch-wise misses and supervise unlabeled pairs. Text-Guided Cross-Modal Instance Augmentation (TG-CMIA) uses a vision-language scene prior to compose tail-class instance pairs while preserving RGB--IR offsets. To the best of our knowledge, ACT is the first framework to study semi-supervised VIOD under this image-pair-level setting. Experiments on DroneVehicle and VEDAI show consistent gains across annotation ratios. With 10\% labeled pairs on DroneVehicle, ACT reaches 94.3\% of the mAP obtained by the same detector under full supervision. Code and models will be available on GitHub to facilitate future work.

Problem

• Visible-infrared object detection
• Semi-supervised learning
• Cross-modal alignment
• Pseudo-label errors
• Tail-class annotations

Innovation

• Cycle-Consistent Region Alignment
• Cross-Modal Consensus Mean-Teacher
• Text-Guided Cross-Modal Instance Augmentation