Interactive Multi-Class Tiny-Object Detection
This addresses the annotation bottleneck for computer vision tasks involving tiny objects, offering a significant efficiency improvement for researchers and practitioners, though it is incremental as it builds on existing interactive detection methods.
The paper tackles the problem of laborious annotation for multiple classes of tiny objects in images by proposing C3Det, an interactive method based on point-based user inputs, which outperforms existing approaches with higher mAP and fewer clicks, and in a user study, it was 2.85x faster and had 0.36x task load compared to manual annotation.
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.