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MaGGIe achieves feature temporal consistency in videos using bidirectional Conv-GRU. It utilizes A100 GPUs and AdamW optimization for robust results.MaGGIe achieves feature temporal consistency in videos using bidirectional Conv-GRU. It utilizes A100 GPUs and AdamW optimization for robust results.

Optimizing Video Matting: Curriculum Learning and Motion Blur Augmentation

Author: HackernoonSource: Hackernoon
2025/12/22 04:00
Blur
BLUR$0.02816-1.40%

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

9.2. Training details

\

:::info 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]).

:::

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

:::

\

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