用幻觉运动增强静态图像弱监督目标检测效果
Enhancing Weakly-Supervised Object Detection on Static Images through (Hallucinated) Motion
- 从静态图生成幻觉运动，用孪生网络学习运动特征
- 在COCO和YouTube-BB上比顶尖方法提升检测精度
- 适合研究弱监督检测与视频线索融合的学者
尽管运动在多种任务中受到关注，其作为静态图像弱监督目标检测（WSOD）模态的潜力仍未被探索。本研究提出一种通过整合运动信息来增强WSOD的方法，利用静态图像生成幻觉运动以提升图像数据集上的检测性能，采用孪生网络实现基于运动的表征学习，通过运动归一化处理相机运动，并根据物体运动情况选择性训练图像。在COCO和YouTube-BB数据集上的实验验证表明，该方法优于当前最先进的方法。
原文摘要 · Abstract (English)
While motion has garnered attention in various tasks, its potential as a modality for weakly-supervised object detection (WSOD) in static images remains unexplored. Our study introduces an approach to enhance WSOD methods by integrating motion information. This method involves leveraging hallucinated motion from static images to improve WSOD on image datasets, utilizing a Siamese network for enhanced representation learning with motion, addressing camera motion through motion normalization, and selectively training images based on object motion. Experimental validation on the COCO and YouTube-BB datasets demonstrates improvements over a state-of-the-art method.
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