Caption Anything: Interactive Image Description with Diverse Multimodal Controls (2305.02677v3)

Abstract: Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, $\textit{e.g.}$, looking at the specified regions or telling in a particular text style. State-of-the-art methods are trained on annotated pairs of input controls and output captions. However, the scarcity of such well-annotated multimodal data largely limits their usability and scalability for interactive AI systems. Leveraging unimodal instruction-following foundation models is a promising alternative that benefits from broader sources of data. In this paper, we present Caption AnyThing (CAT), a foundation model augmented image captioning framework supporting a wide range of multimodel controls: 1) visual controls, including points, boxes, and trajectories; 2) language controls, such as sentiment, length, language, and factuality. Powered by Segment Anything Model (SAM) and ChatGPT, we unify the visual and language prompts into a modularized framework, enabling the flexible combination between different controls. Extensive case studies demonstrate the user intention alignment capabilities of our framework, shedding light on effective user interaction modeling in vision-language applications. Our code is publicly available at https://github.com/ttengwang/Caption-Anything.

• The paper introduces a novel framework, Caption Anything, that employs SAM, BLIP2, and ChatGPT to enable interactive, user-controlled image captioning.
• It uses a triplet architecture—segmenter, captioner, and text refiner—to convert user inputs into precise, context-sensitive image descriptions.
• The approach demonstrates scalable multimodal customization, enhancing caption quality for real-world applications in navigation, education, and accessibility.

An Overview of "Caption Anything: Interactive Image Description with Diverse Multimodal Controls"

This paper presents a novel approach to controllable image captioning through the development of Caption Anything (CAT), a framework utilizing foundation models to enable interactive image description. This work addresses key limitations in the field of Controllable Image Captioning (CIC), primarily the reliance on annotated multimodal data which limits usability and scalability.

Methodology and Framework

Caption Anything leverages pre-existing foundational models to expand the capabilities of image captioning systems. The framework integrates the Segment Anything Model (SAM) and ChatGPT to provide a modular and flexible approach to image description. Specifically, CAT introduces a triplet architecture comprising a segmenter, captioner, and text refiner:

1. Segmenter: Utilizes SAM to convert user-interactive visual controls (points, boxes, and trajectories) into pixel-level masks. This step is crucial for accurately focusing on user-specified regions within images.
2. Captioner: Employs models like BLIP2 to generate raw captions from the visual data and masks. A visual chain-of-thought technique is implemented to ensure the model remains focused on user-indicated objects, thereby improving caption quality.
3. Text Refiner: Refinement of raw captions takes place using LLMs, specifically ChatGPT, to align the output with user-defined linguistic preferences, such as sentiment, length, language, or factuality.

Experimental Results and Capabilities

The paper provides extensive qualitative evidence of CAT's capabilities, showcasing its adaptability across various use cases. CAT demonstrates the ability to diversify multimodal controls and produce detailed, user-aligned descriptions. The framework effectively supports:

• Visual Controls: Ability to caption any object via point, trajectory, or bounding box controls.
• Language Controls: Output customization with sentiment, factuality, and language specifications.
• Object-centric Chatting: Utilizes visual AI APIs for detailed object-specific dialogues.
• Paragraph Captioning: Generates comprehensive scene narratives by synthesizing detailed captions and integrated OCR outputs.

Implications and Future Directions

Caption Anything provides a scalable and adaptable framework for interactive image description, offering a robust platform for real-world applications such as visual navigation, education, and accessibility tools. By leveraging pre-trained models rather than human-annotated datasets, CAT reduces data dependencies, enhancing the flexibility and expansiveness of controllable image captioning techniques.

The framework’s dependency on foundation models facilitates significant transferability and adaptability, marking a shift towards more interactive AI systems capable of understanding and aligning with diverse user intents. Future research could explore expanding the control signal dimensions and further optimizing the integration between multimodal controls, potentially through advancements in the underlying foundation models.

In conclusion, this work represents a significant step toward more interactive and user-centered image captioning systems, offering a solid base for subsequent research and development in the field of vision-language learning.