MaGGIe Roadmap: Overcoming Data Generalization in Matting Models | HackerNoon

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

6. Discussion

Limitation and Future work. Our MaGGIe demonstrates effective performance in human video instance matting with binary mask guidance, yet it also presents opportunities for further research and development. One notable limitation is the reliance on one-hot vector representation for each location in the guidance mask, necessitating that each pixel is distinctly associated with a single instance. This requirement can pose challenges, particularly when integrating instance masks from varied sources, potentially leading to misalignments in certain regions. Additionally, the use of composite training datasets may constrain the model’s ability to generalize effectively to natural, real-world scenarios. While the creation of a comprehensive natural dataset remains a valuable goal, we propose an interim solution: the utilization of segmentation datasets combined with self-supervised or weakly-supervised learning techniques. This approach could enhance the model’s adaptability and performance in more diverse and realistic settings, paving the way for future advancements in the field.

Conclusion. Our study contributes to the evolving field of instance matting, with a focus that extends beyond human subjects. By integrating advanced techniques like transformer attention and sparse convolution, MaGGIe shows promising improvements over previous methods in detailed accuracy, temporal consistency, and computational efficiency for both image and video inputs. Additionally, our approach in synthesizing training data and developing a comprehensive benchmarking schema offers a new way to evaluate the robustness and effectiveness of models in instance matting tasks. This work represents a step forward in video instance matting and provides a foundation for future research in this area.

Acknowledgement. We sincerely appreciate Markus Woodson for the invaluable initial discussions. Additionally, I am deeply thankful to my wife, Quynh Phung, for her meticulous proofreading and feedback.

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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]).

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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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