FocalClick: Towards Practical Interactive Image Segmentation (2204.02574v2)

Abstract: Interactive segmentation allows users to extract target masks by making positive/negative clicks. Although explored by many previous works, there is still a gap between academic approaches and industrial needs: first, existing models are not efficient enough to work on low power devices; second, they perform poorly when used to refine preexisting masks as they could not avoid destroying the correct part. FocalClick solves both issues at once by predicting and updating the mask in localized areas. For higher efficiency, we decompose the slow prediction on the entire image into two fast inferences on small crops: a coarse segmentation on the Target Crop, and a local refinement on the Focus Crop. To make the model work with preexisting masks, we formulate a sub-task termed Interactive Mask Correction, and propose Progressive Merge as the solution. Progressive Merge exploits morphological information to decide where to preserve and where to update, enabling users to refine any preexisting mask effectively. FocalClick achieves competitive results against SOTA methods with significantly smaller FLOPs. It also shows significant superiority when making corrections on preexisting masks. Code and data will be released at github.com/XavierCHEN34/ClickSEG

• The paper introduces a dual-inference mechanism that refines segmentation masks locally, offering a practical solution for low-resource devices.
• It reduces computational costs by decomposing full-image segmentation into efficient inferences on smaller, focused image crops.
• The model achieves competitive accuracy on benchmarks like DAVIS and GrabCut while establishing a new standard for interactive mask correction.

Interactive Image Segmentation Through Efficient Localized Refinement: An Overview of FocalClick

The manuscript "FocalClick: Towards Practical Interactive Image Segmentation" introduces an innovative approach to interactive image segmentation, a field that has garnered substantial interest from academia and industry. The core proposition of FocalClick is to bridge the existing gap between academic solutions and practical demands, such as computational efficiency on low-power devices and the ability to integrate corrections into preexisting masks.

Core Contributions and Methodology

The authors highlight two primary shortcomings of prevailing interactive segmentation methods: high computational demands unsuitable for low-energy devices and an inability to refine existing segmentation masks without inadvertently altering accurate areas. The FocalClick technique tackles these challenges by introducing localized mask updates, shifting away from the conventional approach of full-image prediction.

FocalClick employs a dual-inference mechanism composed of a coarse segmentation of a broader target area followed by a local refinement within a focus area. This mechanism is underpinned by the decomposition of computationally expensive full-image segmentation into two efficient inferences on smaller image crops, significantly reducing FLOPs compared to state-of-the-art (SOTA) methods. Moreover, to enhance compatibility with preexisting masks, the paper introduces the concept of Progressive Merge, intelligently differentiating regions to be preserved and those to be updated, which ensures precise mask refinement.

Quantitative Evaluation

The paper reports competitive performance with a substantial reduction in computational overhead: FocalClick achieves SOTA level accuracy with dramatically reduced FLOPs, translating into faster processing times on devices with limited computing power. Numerous experiments on datasets like GrabCut, Berkeley, SBD, and DAVIS affirm that FocalClick maintains competitive performance levels, while the novel DAVIS-585 benchmark demonstrates the model's enhanced utility in scenarios of mask correction where initial coarse masks are present.

Implications and Future Directions

The potential implications of FocalClick lie in its ability to make interactive segmentation more accessible and feasible for low-resource applications, such as real-time editing on consumer-grade devices or deployment in edge computing environments. The model serves as a versatile tool for digital annotation across various industries, including entertainment, medical imaging, and autonomous systems.

In terms of theoretical contributions, FocalClick introduces the task of Interactive Mask Correction, an unexplored yet practically significant dimension of interactive segmentation that could drive future research. The authors propose a benchmark for evaluating these capabilities, setting a precedent for evaluating similar models and potentially improving the granularity of segmentation tasks.

Looking forward, the adoption of novel training datasets, more refined architectural tweaks, and leveraging matting technologies could address the limitations in FocalClick's performance over intricate object details, such as those highlighted in the "failure cases." Additionally, integrating faster data handling strategies could further optimize the user experience on high-resolution images.

In conclusion, "FocalClick" delineates a practical leap in interactive segmentation, addressing both efficiency and usability, and paving the way for further advancements and real-world applications in AI-driven image processing.