To address the lack of public task-specific data, MaGGIe utilizes synthesized training sets from instance-agnostic sources for robust evaluation and generalizationTo address the lack of public task-specific data, MaGGIe utilizes synthesized training sets from instance-agnostic sources for robust evaluation and generalization

Data Strategy for MaGGIe: Bridging the Gap in Matting Resources

2025/12/18 00:00
1 min read
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Abstract and 1. Introduction

  1. Related Works

  2. MaGGIe

    3.1. Efficient Masked Guided Instance Matting

    3.2. Feature-Matte Temporal Consistency

  3. Instance Matting Datasets

    4.1. Image Instance Matting and 4.2. Video Instance Matting

  4. Experiments

    5.1. Pre-training on image data

    5.2. Training on video data

  5. Discussion and References

\ Supplementary Material

  1. Architecture details

  2. Image matting

    8.1. Dataset generation and preparation

    8.2. Training details

    8.3. Quantitative details

    8.4. More qualitative results on natural images

  3. Video matting

    9.1. Dataset generation

    9.2. Training details

    9.3. Quantitative details

    9.4. More qualitative results

4. Instance Matting Datasets

This section outlines the datasets used in our experiments. With the lack of public datasets for the instance matting task, we synthesized training data from existing public instance-agnostic sources. Our evaluation combines synthetic and natural sets to assess the model’s robustness and generalization.

\ Figure 3. Variations of Masks for the Same Image in MHIM2K Dataset. Masks generated using R50-C4-3x, R50-FPN3x, R101-FPN-400e MaskRCNN models trained on COCO. (Optimal in color).

\

:::info Authors:

(1) Chuong Huynh, University of Maryland, College Park (chuonghm@cs.umd.edu);

(2) Seoung Wug Oh, Adobe Research (seoh,jolee@adobe.com);

(3) Abhinav Shrivastava, University of Maryland, College Park (abhinav@cs.umd.edu);

(4) Joon-Young Lee, Adobe Research (jolee@adobe.com).

:::


:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.

:::

\

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