Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
This work addresses the bottleneck of dataset annotation for computer vision researchers and practitioners, offering incremental improvements to an existing interactive method.
The paper tackles the time-consuming problem of manually labeling object masks in datasets by introducing Polygon-RNN++, an interactive annotation tool that improves upon Polygon-RNN with a new CNN encoder, reinforcement learning training, and a Graph Neural Network for higher resolution. It shows a 10% absolute and 16% relative improvement in mean IoU on Cityscapes and reduces annotator clicks by 50% in interactive mode.
Manually labeling datasets with object masks is extremely time consuming. In this work, we follow the idea of Polygon-RNN to produce polygonal annotations of objects interactively using humans-in-the-loop. We introduce several important improvements to the model: 1) we design a new CNN encoder architecture, 2) show how to effectively train the model with Reinforcement Learning, and 3) significantly increase the output resolution using a Graph Neural Network, allowing the model to accurately annotate high-resolution objects in images. Extensive evaluation on the Cityscapes dataset shows that our model, which we refer to as Polygon-RNN++, significantly outperforms the original model in both automatic (10% absolute and 16% relative improvement in mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We further analyze the cross-domain scenario in which our model is trained on one dataset, and used out of the box on datasets from varying domains. The results show that Polygon-RNN++ exhibits powerful generalization capabilities, achieving significant improvements over existing pixel-wise methods. Using simple online fine-tuning we further achieve a high reduction in annotation time for new datasets, moving a step closer towards an interactive annotation tool to be used in practice.