Boundary-preserving Mask R-CNN (2007.08921v1)

Abstract: Tremendous efforts have been made to improve mask localization accuracy in instance segmentation. Modern instance segmentation methods relying on fully convolutional networks perform pixel-wise classification, which ignores object boundaries and shapes, leading coarse and indistinct mask prediction results and imprecise localization. To remedy these problems, we propose a conceptually simple yet effective Boundary-preserving Mask R-CNN (BMask R-CNN) to leverage object boundary information to improve mask localization accuracy. BMask R-CNN contains a boundary-preserving mask head in which object boundary and mask are mutually learned via feature fusion blocks. As a result, the predicted masks are better aligned with object boundaries. Without bells and whistles, BMask R-CNN outperforms Mask R-CNN by a considerable margin on the COCO dataset; in the Cityscapes dataset, there are more accurate boundary groundtruths available, so that BMask R-CNN obtains remarkable improvements over Mask R-CNN. Besides, it is not surprising to observe that BMask R-CNN obtains more obvious improvement when the evaluation criterion requires better localization (e.g., AP$_{75}$) as shown in Fig.1. Code and models are available at \url{https://github.com/hustvl/BMaskR-CNN}.

Boundary-preserving Mask R-CNN: Enhancing Instance Segmentation with Boundary Information

The research article "Boundary-preserving Mask R-CNN" introduces an innovative approach to improving mask localization accuracy in instance segmentation, a challenging task in computer vision dedicated to categorizing and localizing each object within images at the pixel level. While conventional methods have primarily relied on fully convolutional networks (FCNs) for pixel-wise classification, they have frequently disregarded crucial boundary information, leading to inaccurate and indistinct mask predictions. To combat this issue, the authors propose a novel Boundary-preserving Mask R-CNN (BMask R-CNN) architecture that explicitly incorporates boundary information to refine mask predictions.

Core Contributions

The paper's principal contribution lies in integrating boundary prediction within the Mask R-CNN framework to achieve more accurate instance segmentation. The authors substitute the existing mask head with a boundary-preserving one, featuring two sub-networks for concurrently learning both object masks and boundaries. This dual learning process ensures that the predicted masks are more precisely aligned with object boundaries, thereby enhancing the overall segmentation accuracy.

Key to this architecture is the use of feature fusion blocks, which serve to mutually enhance the learning of boundary and mask features. By incorporating boundary information, the model gains access to rich localization and shape cues, substantially improving the precision of mask predictions when evaluated on datasets like COCO and Cityscapes.

Numerical Results

BMask R-CNN demonstrates significant improvements over the conventional Mask R-CNN, with substantial gains in Average Precision (AP) metrics, particularly under strict localization criteria such as the AP$_{75}$. On the COCO dataset, BMask R-CNN surpasses the baseline by margins of $1.7\%$ AP and $2.2\%$ AP on COCO val set and Cityscapes test set, respectively. Notably, the model's effectiveness becomes more pronounced with more precise boundary annotations, as observed in Cityscapes with its detailed boundary groundtruths.

Implications and Future Directions

The introduction of boundary-preserving mechanisms into instance segmentation networks presents notable implications for the field. Practically, BMask R-CNN offers enhanced segmentation capabilities that could benefit applications in autonomous driving, robotics, and image editing, where accurate boundary delineation is paramount.

Theoretically, the work underscores the importance of spatial boundary information in visual perception systems, suggesting that future models should consider augmenting dense prediction tasks with contextual boundary signals. As instance segmentation methodologies evolve, the boundaries between distinct instances stand to become even more critical, calling for further research into the integration of boundary-aware strategies.

Future developments may look into the synergistic potential of BMask R-CNN with cutting-edge architectures or incorporate additional spatial cues beyond boundaries for an even more nuanced understanding of complex visual scenes. The enhancement of BMask R-CNN with components like Cascade Mask R-CNN could yield further performance benefits, illustrating the model's adaptability and potential for integration into a broader range of segmentational frameworks.