Survey on Argus Framework for Vision Task Generalization

Argus presents a modular and scalable framework designed to efficiently manage a broad spectrum of computer vision tasks through specialized expert models. At the core, the system employs Multimodal Large Language Models (MLLMs) as instruction-driven routers that intelligently delegate tasks such as image generation, object detection, video analysis, and 3D transformation. Unlike conventional monolithic approaches that struggle with task diversity[1], Argus enables flexible and optimized task handling by adopting a controlled, modular architecture. The framework is trained using supervised learning for vision-language tasks and further refined through reinforcement learning[2], improving routing strategies and overall execution. Task-specific routing tokens are incorporated to support multi-step workflows, allowing sequential task execution from a single user instruction. Key machine learning methods, including the Adam optimizer for efficient convergence and cross-entropy loss for accurate task-specific optimization, form the foundation of the training process. The architecture also allows seamless integration of new expert models, ensuring adaptability, scalability, and computational efficiency. With its modular design, Argus provides a robust and future-ready solution that can evolve alongside advancements in the computer vision domain.