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-In-Context Matting: Automatic Alpha Matte Estimation
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+ In-Context Matting
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- In-Context Matting: Automatic Alpha Matte Estimation
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+ In-Context Matting
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+ He Guo1
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+ Zixuan Ye1
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+ Zhiguo Cao1
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+ Hao Lu1
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- Abstract
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- Acknowledgements
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+ 1Huazhong University of Science and Technology
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+ In-context matting enables automatic natural image matting of target images of a certain object category conditioned on a reference image of the same category, with user-provided priors such as masks and scribbles on the reference image only. Notice that, our approach exhibits remarkable cross-domain matting quality.
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+ Abstract
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+ Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data can be generated infinitely using generative models such as DALL-E and diffusion models, with minimal effort and cost. In this paper, we present DatasetDM, a generic dataset generation model that can produce diverse synthetic images and the corresponding high-quality perception annotations (e.g., segmentation masks, and depth). Our method builds upon the pre-trained diffusion model and extends text-guided image synthesis to perception data generation. We show that the rich latent code of the diffusion model can be effectively decoded as accurate perception annotations using a decoder module. Training the decoder only needs less than 1% (around 100 images) manually labeled images, enabling the generation of an infinitely large annotated dataset. Then these synthetic data can be used for training various perception models for downstream tasks. To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation. Notably, it achieves 1) state-of-the-art results on semantic segmentation and instance segmentation; 2) significantly more robust on domain generalization than using the real data alone; and state-of-the-art results in zero-shot segmentation setting; and 3) flexibility for efficient application and novel task composition (e.g., image editing).
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+ Synthetic Data from DatasetDM (supported six task, include long-tail segmentation)
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+ How to do it (pipeline)
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+ DatasetDM consists of two main steps: 1) Training. Using diffusion inversion to extract the latent code from a small amount of data (around hundreds of pictures) and then train the perception decoder. 2) Text-guided data generation. A large language model such as GPT-4 is utilized to prompt infinite and diverse data generation for various downstream tasks.
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+ Perception Decoder
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+ The proposed decoder is a generalized architecture for the six supported tasks, with only minor variations required for different downstream applications, i.e., determining whether to activate certain layers.
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+ Prompting Text-Guided Data Generation
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+ We guide GPT-4 to produce diverse, and infinite prompts. For different downstream tasks and datasets, we give different guided prompts for GPT-4. For example, as for the urban scene of Cityscapes, the simple guided prompt is like `Please provide 100 language descriptions of urban driving scenes for the Cityscapes benchmark, containing a minimum of 15 words each. These descriptions will serve as a guide for Stable Diffusion in generating images.` In this approach, we collected L text prompts, which average around 100 prompts for each dataset.
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+ Experiment-1: Instance segmentation on COCO val2017
+ Quantitative result for Instance segmentation on COCO val2017
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+ Experiment-2: Semantic segmentation on VOC 2012
+ Comparison with the previous methods.
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‘R: ’ refers to the number of real data used to train.
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Experiment-3: Semantic segmentation on Cityscapes
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Experiment-4: Human Pose Estimation on COCO val2017
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Experiment-5: Semantic segmentation on DeepFashion-MM
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Experiment-6: Depth Estimation on NYU Depth V2 val dataset
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Experiment-7: Zero-Shot Semantic Segmentation on PASCAL VOC 2012
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Experiment-8: Performance for Domain Generalization between different datasets.
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+ Examples of Human-Centric Generated Data for DatasetDM. Our method is capable
+ of generating high-accuracy, high-diversity, and unified perceptual annotations.
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+ Examples of Generated Data for Urban City Scenario from DatasetDM.
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+ Prompts of diffusion model from GPT-4. By providing some simple cues, GPT-4 can
+ generate a vast and diverse array of prompts.
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+ Acknowledgements
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+ Based on a template by
+ Ziyi Li and Richard Zhang.
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