Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Works


MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency


Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting


Experiments
5.1. Pre-training on image data
5.2. Training on video data


Discussion and References Related Works Related Works Related Works MaGGIe
3.1. Efficient Masked Guided Instance Matting
3.2. Feature-Matte Temporal Consistency MaGGIe 3.1. Efficient Masked Guided Instance Matting 3.1. Efficient Masked Guided Instance Matting 3.2. Feature-Matte Temporal Consistency 3.2. Feature-Matte Temporal Consistency Instance Matting Datasets
4.1. Image Instance Matting and 4.2. Video Instance Matting Instance Matting Datasets Instance Matting Datasets 4.1. Image Instance Matting and 4.2. Video Instance Matting 4.1. Image Instance Matting and 4.2. Video Instance Matting Experiments
5.1. Pre-training on image data
5.2. Training on video data Experiments Experiments 5.1. Pre-training on image data 5.1. Pre-training on image data 5.2. Training on video data 5.2. Training on video data Discussion and References Discussion and References Discussion and References Supplementary Material Supplementary Material Architecture details


Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images


Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Architecture details Architecture details Architecture details Image matting
8.1. Dataset generation and preparation
8.2. Training details
8.3. Quantitative details
8.4. More qualitative results on natural images Image matting 8.1. Dataset generation and preparation 8.1. Dataset generation and preparation 8.2. Training details 8.2. Training details 8.3. Quantitative details 8.3. Quantitative details 8.4. More qualitative results on natural images 8.4. More qualitative results on natural images Video matting
9.1. Dataset generation
9.2. Training details
9.3. Quantitative details
9.4. More qualitative results Video matting 9.1. Dataset generation 9.1. Dataset generation 9.2. Training details 9.2. Training details 9.3. Quantitative details 9.3. Quantitative details 9.4. More qualitative results 9.4. More qualitative results 5.1. Pre-training on image data Quantitative results. We evaluated our model against previous baselines after retraining them on our I-HIM50K dataset. Besides original works, we modified SparseMat’s Quantitative results. first layer to accept a single mask input. Additionally, we expanded MGM to handle up to 10 instances, denoted as MGM⋆ . We also include the public weights of InstMatt [49] and MGM-in-the-wild [39]. The performance with different masks M-HIM2K are reported in Table 5. The public InstMatt showed the best performance, but this comparison may not be entirely fair as it was trained on private external data. Our model demonstrated comparable results on composite and natural sets, achieving the lowest error in most metrics. MGM⋆ also performed well, suggesting that processing multiple masks simultaneously can facilitate instance interaction, although this approach slightly impacted the Grad metric, which reflects the output’s detail. We also measure the memory and speed of models on M-HIM2K natural set in Fig. 4. While InstMatt, MGM, and SparseMat have the inference time increasing linearly to the number of instances, MGM⋆ and ours keep steady performance in both memory and speed. Qualitative results. MaGGIe’s ability to capture fine details and effectively separate instances is showcased in Fig. 5. At the exact resolution, our model not only achieves highly detailed outcomes comparable to running MGM separately for each instance but also surpasses both Qualitative results. the public and retrained versions of InstMatt. A key strength of our approach is its proficiency in distinguishing between different instances. This is particularly evident when compared to MGM, where we observed overlapping instances, and MGM⋆ , which has noise issues caused by processing multiple masks simultaneously. Our model’s refined instance separation capabilities highlight its effectiveness in handling complex matting scenarios. 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). Authors: 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). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

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Efficient Instance Matting: Analyzing Speed, Memory, and Accuracy in MaGGIe

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