Data Strategy for MaGGIe: Bridging the Gap in Matting Resources

Written by instancing | Published 2025/12/17
Tech Story Tags: deep-learning | model-generalization | instance-matting-datasets | synthetic-data-generation | training-data-synthesis | public-source-adaptation | robustness-assessment | maggie-experiments

TLDRTo address the lack of public task-specific data, MaGGIe utilizes synthesized training sets from instance-agnostic sources for robust evaluation and generalization.via the TL;DR App

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.

Authors:

(1) Chuong Huynh, University of Maryland, College Park ([email protected]);

(2) Seoung Wug Oh, Adobe Research (seoh,[email protected]);

(3) Abhinav Shrivastava, University of Maryland, College Park ([email protected]);

(4) Joon-Young Lee, Adobe Research ([email protected]).

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

Written by instancing | Pioneering instance management, driving innovative solutions for efficient resource utilization, and enabling a more sus
Published by HackerNoon on 2025/12/17
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