  Over the past few years, there has been growing interest in developing a
broad, universal, and general-purpose computer vision system. Such systems have
the potential to address a wide range of vision tasks simultaneously, without
being limited to specific problems or data domains. This universality is
crucial for practical, real-world computer vision applications. In this study,
our focus is on a specific challenge: the large-scale, multi-domain universal
object detection problem, which contributes to the broader goal of achieving a
universal vision system. This problem presents several intricate challenges,
including cross-dataset category label duplication, label conflicts, and the
necessity to handle hierarchical taxonomies. To address these challenges, we
introduce our approach to label handling, hierarchy-aware loss design, and
resource-efficient model training utilizing a pre-trained large vision model.
Our method has demonstrated remarkable performance, securing a prestigious
second-place ranking in the object detection track of the Robust Vision
Challenge 2022 (RVC 2022) on a million-scale cross-dataset object detection
benchmark. We believe that our comprehensive study will serve as a valuable
reference and offer an alternative approach for addressing similar challenges
within the computer vision community. The source code for our work is openly
available at https://github.com/linfeng93/Large-UniDet.
