MaGGIe Architecture: Efficient Mask-Guided Instance Matting

Table of Links

Abstract and 1. Introduction

2. Related Works

3. MaGGIe

   3.1. Efficient Masked Guided Instance Matting

   3.2. Feature-Matte Temporal Consistency

4. Instance Matting Datasets

   4.1. Image Instance Matting and 4.2. Video Instance Matting

5. Experiments

   5.1. Pre-training on image data

   5.2. Training on video data

6. Discussion and References

Supplementary Material

7. Architecture details

8. Image matting

   8.1. Dataset generation and preparation

   8.2. Training details

   8.3. Quantitative details

   8.4. More qualitative results on natural images

9. Video matting

   9.1. Dataset generation

   9.2. Training details

   9.3. Quantitative details

   9.4. More qualitative results

3. MaGGIe

We introduce our efficient instance matting framework guided by instance binary masks, structured into two parts. The first Sec. 3.1 details our novel architecture to maintain accuracy and efficiency. The second Sec. 3.2 describes our approach for ensuring temporal consistency across frames in video processing.

3.1. Efficient Masked Guided Instance Matting

In cross-attention (CA), Q and (K, V) originate from different sources, whereas in self-attention (SA), they share similar information.

where {; } denotes concatenation along the feature dimension, and G is a series of sparse convolutions with sigmoid activation.

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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).

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

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