E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation (2203.04074v1)

Abstract: Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of hand-crafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for 512*512 images on an NVIDIA A6000 GPU. Code will be released at https://github.com/zhang-tao-whu/e2ec.

• The paper introduces a learnable contour initialization, multi-direction alignment, and dynamic matching loss to overcome traditional contour method limitations.
• The method achieves state-of-the-art performance on datasets like KINS, SBD, Cityscapes, and COCO with an impressive 36 fps inference speed.
• The innovations provide clearer boundaries and faster convergence, paving the way for real-time applications in autonomous driving and robotic vision.

Overview of E2EC: An End-to-End Contour-based Method for Instance Segmentation

This paper presents E2EC, a novel end-to-end contour-based method for high-quality instance segmentation, addressing the limitations of existing contour-based methods. Traditional contour-based methods, while avoiding the intensive pixel-wise processing of mask-based approaches, rely heavily on both handcrafted contour initialization and fixed vertex pairing strategies. These limitations contribute to increased model complexity, reducing performance, and increasing learning difficulty.

The proposed E2EC introduces three critical innovations to enhance performance in terms of both precision and speed:

1. Learnable Contour Initialization: Unlike previous methods relying on manually designed initial contours, E2EC employs a learnable contour initialization architecture. This improvement alleviates the discrepancies between initial and ground-truth contours, enabling more efficient deformation paths.
2. Multi-Direction Alignment (MDA): E2EC reduces the learning difficulty by applying a novel label sampling scheme, multi-direction alignment, which optimally aligns the predicted vertices with label vertices by fixing directions relative to a center point.
3. Dynamic Matching Loss (DML): E2EC employs a dynamic matching strategy to determine the most appropriate pairing between predicted and ground-truth vertices, which refines the boundary details and convergence.

The experimental results indicate that E2EC achieves state-of-the-art accuracy on the KITTI INStance (KINS), Semantic Boundaries Dataset (SBD), Cityscapes, and COCO datasets, with a significant inference speed of 36 fps for 512×512 input images on an NVIDIA A6000 GPU. The results show a marked improvement over prior methods such as Deep Snake, in terms of both the mask quality (AP$^{msk}$) and boundary quality (AP$^{bdy}$).

Technical Developments

The primary technical advancement in this work, the learnable contour initialization, integrates contour learning directly into the network architecture, bypassing the limitations of static or manually adjusted contours. This design enables direct regression of the initial offsets from the center point, based on its features. Furthermore, the global contour deformation module propagates features globally across vertices, improving the correction of contour prediction errors that might arise from localized features.

The multi-direction alignment (MDA) enhances the robustness of contour prediction by ensuring vertex stability relative to the center of mass of objects, allowing contour shapes to adjust more naturally to the annotated ground-truth without losing vertex ordering or increasing computational complexity.

The introduction of a dynamic matching loss function reduces the dependency on fixed vertex pairing, allowing dynamic optimization during training by adjusting predicted vertices to be closer to their actual positions within the segmented boundary. This novel loss framework results in sharper and more precise object boundaries compared with conventional static loss functions.

Implications and Future Developments

E2EC acts as a pivotal step forward in contour-based instance segmentation, allowing for broader applications in real-time environments such as autonomous driving and robotic vision systems. By addressing the limitations of existing methods with a learnable and data-driven contour adaptation framework, the door is opened for more advanced and scalable models.

Future research can build upon the architectures within E2EC for further enhancement. Exploring parallel computing optimizations and more holistic integration with other deterministic or probabilistic models could lead to even faster processing times and higher accuracy. Additionally, adapting the techniques suggested in E2EC to more generalized contexts, including video processing or 3D model segmentation, could augment the applicability of contour-based methods in diverse machine learning and AI tasks. Overall, E2EC establishes an essential groundwork that is likely to spur further innovations and cross-sectoral applications in the field of computer vision.