Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing (2412.19806v1)

Abstract: Recent developments of vision LLMs have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained instance-level understanding, lack of unified support for both images and videos, and insufficient coverage across various vision tasks. In this paper, we present VITRON, a universal pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing of both static images and dynamic videos. Building on top of an LLM backbone, VITRON incorporates encoders for images, videos, and pixel-level regional visuals within its frontend modules, while employing state-of-the-art visual specialists as its backend, via which VITRON supports a spectrum of vision end tasks, spanning visual comprehension to visual generation, from low level to high level. To ensure an effective and precise message passing from LLM to backend modules for function invocation, we propose a novel hybrid method by simultaneously integrating discrete textual instructions and continuous signal embeddings. Further, we design various pixel-level spatiotemporal vision-language alignment learning for VITRON to reach the best fine-grained visual capability. Finally, a cross-task synergy module is advised to learn to maximize the task-invariant fine-grained visual features, enhancing the synergy between different visual tasks. Demonstrated over 12 visual tasks and evaluated across 22 datasets, VITRON showcases its extensive capabilities in the four main vision task clusters. Overall, this work illuminates the great potential of developing a more unified multimodal generalist. Project homepage: https://vitron-LLM.github.io/

• The paper demonstrates that Vitron integrates four key visual tasks—understanding, generation, segmentation, and editing—within one unified LLM framework.
• It utilizes a hybrid message-passing scheme and CLIP-based encoders alongside specialized backend modules for accurate multimodal processing.
• Vitron achieves pixel-level spatiotemporal alignment across 12 tasks on 22 datasets, outperforming several task-specific models.

Vitron: A Unified Pixel-Level Vision LLM

The paper presents Vitron, a comprehensive vision LLM designed to integrate multiple visual tasks into a single framework. The key innovation of Vitron lies in its ability to perform four main categories of visual tasks: understanding, generating, segmenting, and editing, applicable to both static images and dynamic videos. This model represents a significant effort in unifying numerous vision-related functionalities, which have traditionally been handled by specialized models, into a singular system.

Overview

Vitron is structured to overcome several limitations observed in traditional vision LLMs. Notably, it addresses the lack of unified support across different visual media and tasks by incorporating specialized modules effectively within its architecture. At its core, Vitron builds upon an LLM backbone and integrates state-of-the-art (SoTA) visual encoders and decoders for varied tasks, executed through frontend and backend modules.

System Architecture

Frontend Encoding: Vitron utilizes CLIP-based encoders for both images and videos, facilitating the processing of input data into a format compatible with the LLM. This stage ensures that both static and dynamic visual information is accurately represented.

Core LLM: The LLM component processes visual and textual inputs to provide semantic understanding and reasoning. It outputs explicit textual instructions and continuous embeddings to guide subsequent task executions in the backend modules.

Backend Modules: The system employs visual specialists like GLIGEN for image tasks and ZeroScope for video tasks, among others, which are triggered through the LLM's instructions for performing specific operations such as generation and editing.

Methodology

Vitron employs a hybrid message-passing scheme, integrating both discrete textual instructions and continuous signal embeddings. This approach enhances the harmony between the LLM and vision-specific modules, facilitating accurate task execution and robust handling of multimodality.

Moreover, Vitron is trained to achieve pixel-level spatiotemporal vision-language alignment, bolstering its fine-grained perception capabilities. This is critical for achieving tasks that require precise interaction and understanding of visual content at a very detailed level.

Experimental Evaluation

Vitron's capabilities are demonstrated across 12 visual tasks and evaluated on 22 datasets. The model showcases its effectiveness in tasks including image and video segmentation, vision comprehension, and content generation and editing. The experimental results emphasize Vitron's robust performance, often surpassing the results of singular task-specific specialists.

Implications and Future Prospects

The development of Vitron carries significant implications for the field of AI, particularly in advancing toward more comprehensive multimodal AI systems. By unifying diverse visual functionalities, Vitron sets a precedent for the development of more generalist AI systems capable of performing a wide range of tasks seamlessly. This approach not only enhances practical applications in fields requiring intensive visual data interaction but also aligns with theoretical trends seeking to develop holistic AI systems.

Future developments could focus on further optimizing the integration of vision and language for improved scalability and efficiency, potentially incorporating more diverse sensory inputs and achieving better performance with fewer resources. The exploration of more advanced synergy strategies between tasks and refinement of message-passing mechanisms can also contribute to more seamless and accurate task execution.