Annotating Object Instances with a Polygon-RNN (1704.05548v1)

Abstract: We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We further show generalization capabilities of our approach to unseen datasets.

• The paper introduces a Polygon-RNN model that redefines segmentation as a polygon prediction task to significantly reduce manual annotation effort.
• The methodology integrates a CNN and a Convolutional LSTM to predict object boundaries, achieving a 4.7× speed-up and an 82.2% IoU agreement on the Cityscapes dataset.
• The approach demonstrates strong generalizability across datasets and lays the groundwork for human-in-the-loop systems in efficient annotation pipelines.

Annotating Object Instances with a Polygon-RNN

The task of object instance segmentation, a crucial step for various computer vision applications, typically involves labeling images at the pixel level. The process of creating such datasets is labor-intensive and costly, primarily due to the detailed annotations required for effective training of data-hungry models such as those based on deep learning. The paper "Annotating Object Instances with a Polygon-RNN" introduces a novel approach aimed at reducing the burden of manual annotation while maintaining high-quality segmentations. This essay provides an overview of the proposed approach, evaluates its performance, and discusses potential implications and future developments.

Overview of the Approach

The paper proposes a semi-automatic method for the annotation of object instances via a framework called Polygon-RNN. Instead of treating annotation as a pixel-level labeling problem, the authors reframe it as a polygon prediction task. This approach is aligned with the traditional method of annotating datasets using polygons, which typically requires fewer clicks—approximately 30 to 40 per object compared to pixel-level annotations.

The Polygon-RNN model is designed to predict the vertices of polygons that outline objects within image crops. It is implemented using a Convolutional Neural Network (CNN) for feature extraction and a Recurrent Neural Network (RNN) configured as a Convolutional LSTM for vertex prediction. The model allows for human interaction, enabling corrections at any vertex prediction stage, which facilitates precise and efficient annotations.

Performance and Results

The methodology was evaluated on the Cityscapes dataset, a widely used benchmark for urban scene understanding, and demonstrated substantial improvements in annotation efficiency. The approach delivered a speed-up factor of 4.7 on average across all classes, with car annotations witnessing a speed-up of 7.3 while achieving 82.2% agreement in Intersection over Union (IoU) with the original ground truth. This level of performance matches the typical agreement between human annotators.

Furthermore, the method showcases a noteworthy generalizability to unseen datasets, such as KITTI, which contains larger object instances than Cityscapes. This cross-dataset applicability marks a significant advantage of the proposed system, highlighting its robustness and versatility.

Implications and Future Directions

The implications of this research are twofold: practical and theoretical. Practically, the proposed approach substantially reduces the time and cost associated with creating high-quality segmentation datasets, thus facilitating the development and advancement of computer vision models. Theoretically, the integration of human corrections into the annotation workflow enhances the model's ability to produce accurate and coherent object boundaries, which may inspire further exploration into human-in-the-loop systems for various machine learning tasks.

Given the impressive results and potential applications, several avenues for future research are suggested. Enhancing the model's output resolution could address minor quantization errors observed in large objects. Also, exploring more sophisticated user interaction mechanisms and extending the approach to a broader range of objects and environments could solidify its utility and robustness further.

In conclusion, the utilization of a Polygon-RNN for object instance annotation presents a significant step forward in semi-automatic dataset preparation, proposing a scalable solution that aligns with existing dataset annotation practices. This research paves the way for innovations in efficient data labeling, with direct implications for improving machine learning model training in the field of computer vision.