Referring Image Matting (2206.05149v3)

Abstract: Different from conventional image matting, which either requires user-defined scribbles/trimap to extract a specific foreground object or directly extracts all the foreground objects in the image indiscriminately, we introduce a new task named Referring Image Matting (RIM) in this paper, which aims to extract the meticulous alpha matte of the specific object that best matches the given natural language description, thus enabling a more natural and simpler instruction for image matting. First, we establish a large-scale challenging dataset RefMatte by designing a comprehensive image composition and expression generation engine to automatically produce high-quality images along with diverse text attributes based on public datasets. RefMatte consists of 230 object categories, 47,500 images, 118,749 expression-region entities, and 474,996 expressions. Additionally, we construct a real-world test set with 100 high-resolution natural images and manually annotate complex phrases to evaluate the out-of-domain generalization abilities of RIM methods. Furthermore, we present a novel baseline method CLIPMat for RIM, including a context-embedded prompt, a text-driven semantic pop-up, and a multi-level details extractor. Extensive experiments on RefMatte in both keyword and expression settings validate the superiority of CLIPMat over representative methods. We hope this work could provide novel insights into image matting and encourage more follow-up studies. The dataset, code and models are available at https://github.com/JizhiziLi/RIM.

• The paper introduces Referring Image Matting (RIM), a novel approach that uses natural language to precisely isolate target objects in images.
• It presents the RefMatte dataset, comprising 47,500 images across 230 object categories with nearly 475,000 text expressions, to robustly train and evaluate RIM techniques.
• The CLIPMat model incorporates innovative modules—Context-embedded Prompt, Text-driven Semantic Pop-up, and Multi-level Details Extractor—to achieve superior performance in language-driven image matting.

Analysis of "Referring Image Matting"

The paper "Referring Image Matting" introduces a novel approach to image matting, termed Referring Image Matting (RIM). This task diverges from traditional methods by employing natural language descriptions to isolate specific foreground objects within images, thus enhancing user interaction simplicity and specificity in image editing tasks.

The authors identify the limitations of existing matting methodologies, which typically require predefined inputs like scribbles or trimaps, or default to extracting all foreground objects indiscriminately. They propose RIM to address these issues, offering a more flexible, language-guided mechanism capable of dynamically responding to human instructions.

RefMatte Dataset

A critical component of this paper is the introduction of the RefMatte dataset, which is significantly more comprehensive than existing datasets. With 230 object categories, 47,500 images, and nearly 475,000 text expressions, RefMatte provides an extensive foundation for training and evaluating models in the context of RIM. The dataset also includes a real-world subset with 100 high-resolution images to test out-of-domain generalization, a key aspect in assessing the robustness of language-model-driven matting techniques.

CLIPMat Model

To baseline the RIM task, the authors develop a model called CLIPMat. Leveraging the pre-trained CLIP architecture, CLIPMat incorporates several innovative modules:

• Context-embedded Prompt (CP): Utilizes prompt engineering to provide contextual awareness to the language inputs, improving the model's textual comprehension in matting scenarios.
• Text-driven Semantic Pop-up (TSP): Enhances the extraction of high-level visual semantics, crucial for accurately identifying and processing the object of interest in fine detail.
• Multi-level Details Extractor (MDE): Ensures that intricate details are preserved during the matting process, which is particularly challenging for tasks requiring pixel-level precision.

The effectiveness of CLIPMat is empirically validated through comparison with leading models in related tasks, confirming its superior performance in both keyword and expression-based settings on the RefMatte dataset and real-world test examples.

Implications and Future Directions

The practical implications of this research are considerable. RIM promises more intuitive image matting, particularly in applications where user interaction through language is beneficial, such as virtual reality content creation or advanced photo editing tools. The ability to accurately mat objects based on intricate linguistic descriptions enhances accessibility and extends the utility of image matting technologies across diverse domains.

Moreover, the introduction of text-driven processes in matting opens new research areas concerning the integration of multimodal inputs in vision tasks. Future developments could explore enhancing robustness against ambiguous or contextually complex language inputs. Further advancements might also involve refining the detail preservation capability of models, aiming towards seamless integration in real-world, dynamic environments where lighting and background variability present ongoing challenges.

In conclusion, the paper presents a compelling shift in the image matting paradigm, with RefMatte and CLIPMat demonstrating both the viability and advantages of integrating natural language processing into this domain. The contributions laid in this work furnish a strong groundwork for subsequent investigations in multimodal AI and its intersection with computer vision tasks.