In-Context Matting (2403.15789v1)

Abstract: We introduce in-context matting, a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points, scribbles, and masks, in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category, without additional auxiliary input. This setting marries good performance in auxiliary input-based matting and ease of use in automatic matting, which finds a good trade-off between customization and automation. To overcome the key challenge of accurate foreground matching, we introduce IconMatting, an in-context matting model built upon a pre-trained text-to-image diffusion model. Conditioned on inter- and intra-similarity matching, IconMatting can make full use of reference context to generate accurate target alpha mattes. To benchmark the task, we also introduce a novel testing dataset ICM-$57$, covering 57 groups of real-world images. Quantitative and qualitative results on the ICM-57 testing set show that IconMatting rivals the accuracy of trimap-based matting while retaining the automation level akin to automatic matting. Code is available at https://github.com/tiny-smart/in-context-matting

• The paper introduces IconMatting, a model leveraging inter- and intra-context matching with pre-trained diffusion techniques to estimate alpha mattes.
• It presents the ICM-57 dataset, rigorously benchmarking performance on 57 real-world image groups for diverse instance complexities.
• Experimental results show IconMatting achieves trimap-level accuracy while significantly reducing reliance on user-generated auxiliary inputs.

In-Context Matting: A Novel Approach for Image Matting

The paper "In-Context Matting" introduces an innovative paradigm within the field of image matting, titled in-context matting, which fundamentally re-thinks how image matting tasks can be approached by leveraging a reference-based methodology. The authors, affiliated with the Huazhong University of Science and Technology, aim to bridge the gap between accuracy and efficiency, and between customization and automation, in the image matting domain.

At its core, image matting involves estimating an accurate alpha matte by solving the equation $I=\alpha F+(1-\alpha) B$, where $I$ is the observation, and $(\alpha, F, B)$ are unknown elements to be inferred — representing the alpha matte, foreground, and background of the image respectively. The ill-posed nature of this problem has historically necessitated the use of auxiliary inputs, such as trimaps, scribbles, or known backgrounds, to reduce uncertainties. However, the reliance on these auxiliary inputs has often made the process cumbersome and user-intensive.

In response to these challenges, recent advancements have begun exploring automatic matting techniques, which largely eschew auxiliary inputs in favor of models trained on predefined categories (e.g., humans, animals). Despite their ease of use, these models suffer from poor generalization to new or unseen categories. This paper seeks a middle ground: a novel in-context matting framework capable of accurately processing batches of target images based on a single reference image with guided priors, such as masks or scribbles, only provided for the reference image.

Methodology and Contributions

To address the challenges of in-context matting, the authors introduce IconMatting, a model built upon a pre-trained text-to-image diffusion model. Offered within this framework are several key contributions:

1. Inter-Context and Intra-Context Matching: The proposed model leverages Stable Diffusion's inherent capabilities for feature correspondence, introducing inter- and intra-similarity matching to fully utilize reference context for generating target alpha mattes.
2. ICM-57 Dataset: To evaluate their method, the authors craft a novel testing dataset named ICM-57, which covers 57 groups of real-world images, spanning various categories and instance complexities. The dataset is designed to rigorously benchmark in-context matting models.
3. Significant Performance Achievements: Through their experiments, the authors demonstrate that IconMatting performs comparably to trimap-based methods in accuracy while retaining the automation benefits of automatic matting models. Quantitative assessments reveal its efficacy in marrying customization with automation, setting a new precedent in the field.

Implications and Future Work

The implications of this work are manifold. Practically, the framework simplifies the matting process by allowing users to specify the desired matting targets with minimal input, potentially enhancing efficiency and the user experience. Theoretically, it opens new avenues for integrating large-scale pre-trained models, like diffusion models, into discriminative tasks beyond their generative origins.

Looking forward, the authors speculate on a potential expansion of their framework to other vision tasks, such as video object matting, where temporal propagation of context could yield robust object extraction in dynamic scenes. There is also room to explore improved backbone selection or feature adaptation strategies for ever-better foreground matching under diverse, uncontrolled conditions, thus expanding the applicability and performance of in-context matting models further.

In summary, "In-Context Matting" presents a promising direction that could influence future methodologies in image matting, offering a more nuanced understanding of the trade-offs between various matting paradigms and setting the stage for further scholarly exploration in leveraging context for automated vision tasks.