论文标题
交互式多级小对象检测
Interactive Multi-Class Tiny-Object Detection
论文作者
论文摘要
在给定图像中注释数十个或数百个微小的物体对于多种计算机视觉任务而言是费力但至关重要的。这样的图像通常包含来自各个类别的对象，但是迄今为止，尚未探索检测任务的多类交互式注释设置。为了满足这些需求，我们基于一些基于几个基于点的用户输入，为多个类别的多个小型对象的多个实例提出了一种新颖的交互式注释方法。我们的方法C3DET分别通过后期融合和特征相关以本地和全局方式将完整的图像上下文与注释者输入相关联。我们使用两阶段和一阶段对象检测体系结构对小型DOTA和LCELL数据集进行实验，以验证我们方法的功效。我们的方法在交互式注释中的表现优于现有方法，通过更少的点击率获得更高的地图。此外，我们在用户研究中验证方法的注释效率，与手动注释相比，与手动注释相比，该研究表明它的速度更快2.85倍，仅产生0.36倍的任务负荷（NASA-TLX，较低，较低）。该代码可在https://github.com/chungyi347/interactive-multi-class-tiny-object-detection上找到。
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interactive annotation setting for the detection task has thus far been unexplored. To address these needs, we propose a novel interactive annotation method for multiple instances of tiny objects from multiple classes, based on a few point-based user inputs. Our approach, C3Det, relates the full image context with annotator inputs in a local and global manner via late-fusion and feature-correlation, respectively. We perform experiments on the Tiny-DOTA and LCell datasets using both two-stage and one-stage object detection architectures to verify the efficacy of our approach. Our approach outperforms existing approaches in interactive annotation, achieving higher mAP with fewer clicks. Furthermore, we validate the annotation efficiency of our approach in a user study where it is shown to be 2.85x faster and yield only 0.36x task load (NASA-TLX, lower is better) compared to manual annotation. The code is available at https://github.com/ChungYi347/Interactive-Multi-Class-Tiny-Object-Detection.
