EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow (2109.09406v2)

Abstract: High-quality training data play a key role in image segmentation tasks. Usually, pixel-level annotations are expensive, laborious and time-consuming for the large volume of training data. To reduce labelling cost and improve segmentation quality, interactive segmentation methods have been proposed, which provide the result with just a few clicks. However, their performance does not meet the requirements of practical segmentation tasks in terms of speed and accuracy. In this work, we propose EdgeFlow, a novel architecture that fully utilizes interactive information of user clicks with edge-guided flow. Our method achieves state-of-the-art performance without any post-processing or iterative optimization scheme. Comprehensive experiments on benchmarks also demonstrate the superiority of our method. In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks. The source code and tool is avaliable at https://github.com/PaddlePaddle/PaddleSeg.

• The paper introduces an interactive segmentation framework that uses an edge-guided flow mechanism to deliver stable and precise results.
• It employs an early-late fusion strategy and a coarse-to-fine network (CoarseNet and FineNet) to integrate user clicks with image features.
• Benchmark tests on datasets like GrabCut and Pascal VOC show that EdgeFlow attains superior accuracy and efficiency with reduced NoC metrics.

EdgeFlow: Advancements in Interactive Image Segmentation

The paper "EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow" introduces a sophisticated approach to interactive image segmentation. Traditional segmentation methods often require extensive manual annotation, a process that is both costly and labor-intensive. EdgeFlow addresses this by leveraging an interactive segmentation architecture that maximizes the utilization of user inputs, specifically focusing on edge-guided dynamics to enhance precision and stability.

Core Contributions

The EdgeFlow methodology introduces several key innovations:

1. Interactive Architecture: The architecture exploits user clicks and their consecutive relations through an early-late fusion strategy. This approach counters the common issue of feature dilution found in other models that incorporate interactive inputs only at the initial layers.
2. Edge-Guided Flow: By embedding an edge-guided flow mechanism, the model stabilizes the segmentation process. Edge masks, generated from previous user interactions, serve as priors, significantly reducing abrupt changes in segmentation output with additional clicks.
3. Coarse-to-Fine Network Design: The EdgeFlow architecture comprises CoarseNet and FineNet components. CoarseNet processes initial segmentation tasks, while FineNet refines these outputs, ensuring finely detailed segmentation even in challenging images.
4. Efficient Segmentation Tool: The tool developed from this methodology supports not only interactive segmentation but also polygon editing, enhancing annotation flexibility and accuracy. This utility, available via PaddlePaddle, demonstrates practical applicability across a variety of data types.

Performance Analysis

EdgeFlow's performance is benchmarked against several prominent datasets, including GrabCut, Berkeley, DAVIS, and Pascal VOC. The results indicate superior accuracy and efficiency, reflected in lower NoC@85 and NoC@90 metrics compared to existing methods. Notably, EdgeFlow demonstrates noteworthy stability in segmentation results, minimizing sudden performance drops when additional user clicks are introduced.

Methodological Framework

The proposed method integrates deep learning paradigms adopted from image and interactive data fusion, optimized through:

• Feature Fusion: Early-late fusion ensures comprehensive integration of interactive and image features across network stages, preventing early elimination of user-specific data.
• Edge Utilization: Edge masks serve as dynamic inputs that align well with the segmentation task, offering a smoother transition between segmentation states.
• Loss Optimization: The model employs a normalized focal loss to prioritize misclassified pixels, balancing emphasis towards regions needing refinement.

Implications and Future Directions

The implications of EdgeFlow extend to both theoretical and practical realms in AI-driven image processing. Practically, it proposes a scalable solution for domain-specific image annotation tasks by significantly reducing annotation time and effort. Theoretically, it underscores the potential of integrating edge dynamics with interactive inputs for segmentation tasks, opening new avenues in edge-aware neural architectures.

Looking forward, exploration into lightweight models based on EdgeFlow can facilitate deployment across diverse platforms, including mobile and embedded systems. Additionally, integrating multi-modal inputs like audio and text could further enhance the interactive segmentation landscape, leveraging various data inputs for richer contextual understanding.

The EdgeFlow approach thus constitutes a significant advancement in interactive segmentation, offering robust solutions with enhanced user adaptability and stable performance across variable datasets. Such developments underscore the growing potential for automated yet interactive systems in the evolving field of computer vision.