LLaVA-Interactive: A Versatile Multimodal Human and AI Interaction Prototype

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Nov 5 2023

In an article recently submitted to the ArXiV* server, researchers introduced LLaVA-Interactive, an advanced multimodal human and artificial intelligence (AI) interaction prototype. This system engages in multi-turn dialogues, utilizing multimodal user inputs and responses. It distinguishes by enabling visual and language prompts, aligning with users' intentions.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

LLaVA-Interactive efficiently combines three pre-built AI models, each with unique multimodal skills: Visual chat from Likely Learner for Visual and Audio (LLaVA), image segmentation from Scalable and Efficient Image Manipulation (SEEM), and image generation/editing from Generative Latent Image Generator (GLIGEN). The article demonstrates diverse application scenarios of LLaVA-Interactive, highlighting the system's potential and inspiring future research in multimodal interactive systems.

Prior Research

Past studies have explored Language-Modeling Multimodalities (LMMs) to generate visual output and enable interactive features. The existing LMMs have extended their capabilities to support image output like generation and segmentation. Projects in this field, such as Visual Chat Generative Pre-Trained Transformer (ChatGPT), Extended GPT (X-GPT), and Multimodal Responsive Engagement and Articulated Coordination in Task-oriented Dialogue (MM-REACT), activate expert vision models with image output during inference.

LLaVA-Interactive differentiates itself by cost-effective development, combining three models for visual interaction without additional training or prompt engineering. It also emphasizes user-driven visual interaction, allowing users to draw strokes to specify intent in segmentation and editing.

LLaVA-Interactive: Enhancing Visual Interaction

The user interface of LLaVA-Interactive comprises three main panels, each annotated with distinct colors for clarity. The top-left panel in purple maintains the current image and accepts visual prompts like user-drawn strokes. The green panel on the right serves as a language-based chat interface for user questions regarding the image. The lower-left section, highlighted in blue, is the visual interaction interface, which consists of three tabs, each designated by a red rounded rectangle.

Providing examples illustrates how users can interact with LLaVA-Interactive through visual prompts. Users can remove or change objects by drawing strokes on the object of interest and subsequently using the "Segment" and "Generate" buttons to modify the image. Users can imprint new objects by specifying object configurations with bounding boxes and providing semantic concepts. Users can generate new images by sketching object layouts on the "Sketch Pad" and providing image-level captions.

The workflow of LLaVA-Interactive depicts the typical visual creation process. Users start with an image, either by uploading one or generating it using language captions and bounding boxes to arrange objects. They can interact with the image through visual chat, segmentation, or editing. Users can inquire, edit, construct object masks, or introduce new objects to the image, and they can iteratively repeat this interactive process. LLaVA-Interactive enhances LLaVA's capabilities, enabling it to support visual interaction through user-drawn strokes and bounding boxes and facilitating optical image generation and editing.

The development of LLaVA-Interactive involved overcoming several technical challenges. These challenges included enhancing the GLIGEN inpainting model, addressing user interaction limitations in the Gradio framework, managing complex project integrations, and handling package requirements and dependencies efficiently. The development process resulted in a cost-effective system that combines existing model checkpoints without additional training.

LLaVA-Interactive: A Multifaceted AI Solution

In the diverse landscape of AI-assisted applications, LLaVA-Interactive shines as a versatile tool. It enables users to co-create visual scenes and descriptions, making it invaluable for content creators. Whether crafting serene outdoor landscapes or engaging in graphic design for Halloween posters, users can collaboratively refine their creations. This iterative process empowers users to request adjustments and receive feedback to perfect their visual narratives. Moreover, LLaVA-Interactive extends its capabilities to personalized kid's clothing design, aiding users in enhancing their designs and boosting their confidence.

Food preparation and storytelling are also areas where this AI assistant excels. Users seeking culinary guidance can turn to LLaVA-Interactive for suggestions and enhancements, ensuring a memorable dining experience. In storytelling, the AI provides detailed descriptions and the flexibility to adapt visuals, allowing users to craft whimsical narratives. Furthermore, LLaVA-Interactive plays a vital role in scientific education, making learning enjoyable for children by presenting complex concepts through relatable imagery. It enhances cartoon interpretation skills by highlighting the importance of context within images.

Finally, the AI's applications extend to interior design, offering solutions for large and small living spaces. Users can receive expert advice and creatively adjust their living room designs. Additionally, it excels in identifying unusual and risky items in images, contributing to safety and security by detecting potential threats and anomalies. In a world of diverse applications, LLaVA-Interactive stands as a powerful tool for enhancing creativity, learning, and safety across a broad spectrum of scenarios.

Conclusion

To sum up, this paper introduces LLaVA-Interactive, a cost-effective research demo prototype that showcases the practical applications of large multimodal models for visual input, output, and interaction. LLaVA-Interactive combines three pre-trained multimodal models, namely LLaVA, SEEM, and GLIGEN, to create a fully vision-language multimodal system capable of performing various complex tasks.

While the system's abilities are contingent on the performance of these pre-trained models, future research avenues include enhancing specific skills by updating or creating improved individual models and developing more unified multimodal foundation models to enable the emergence of new capabilities through latent task composition.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.