Title: Q-Router: Agentic Video Quality Assessment with Expert Model Routing
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Video Quality Assessment, Agent, VLM

Abstract: Video quality assessment (VQA) is a fundamental computer vision task that aims to predict the perceptual quality of a given video in alignment with human judgments. Existing performant VQA models trained with direct score supervision suffer from **(1)** *poor generalization* across diverse content and tasks, ranging from user-generated content (UGC), short-form videos, to AI-generated content (AIGC), **(2)** *limited interpretability*, and **(3)** *lack of extensibility* to novel use cases or content types. We propose Q-Router, an agentic framework for universal VQA with a multi-tier model routing system. Q-Router integrates a diverse set of expert models and employs vision–language models (VLMs) as real-time routers that dynamically reason then ensemble the most appropriate experts conditioned on the input video semantics.  We build a multi-tiered routing system based on the computing budget, with the heaviest tier involving a specific spatiotemporal artifacts localization for interpretability. This agentic design enables Q-Router to combine the complementary strengths of specialized experts, achieving both flexibility and robustness in delivering consistent performance across heterogeneous video sources and tasks.  Extensive experiments demonstrate that Q-Router matches or surpasses state-of-the-art VQA models on a variety of benchmarks, while substantially improving generalization and interpretability. Moreover, Q-Router excels on the quality-based question answering benchmark, Q-Bench-Video, highlighting its promise as a foundation for next-generation VQA systems. Finally, we show that Q-Router can localize spatio-temporal artifact/hallucination localization, showing potential as a reward function for post-training video generation models.