UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended
  Language Interface
Authors: name: Hao Tang, name: Chenwei Xie, name: Haiyang Wang, name: Xiaoyi Bao, name: Tingyu Weng, name: Pandeng Li, name: Yun Zheng, name: Liwei Wang

Generalist models have achieved remarkable success in both language and
vision-language tasks, showcasing the potential of unified modeling. However,
effectively integrating fine-grained perception tasks like detection and
segmentation into these models remains a significant challenge. This is
primarily because these tasks often rely heavily on task-specific designs and
architectures that can complicate the modeling process. To address this
challenge, we present \ours, a framework that Unifies
Fine-grained visual perception tasks through an Open-ended
language interface. By transforming all perception targets into the language
space, \ours unifies object-level detection, pixel-level segmentation, and
image-level vision-language tasks into a single model. Additionally, we
introduce a novel embedding retrieval approach that relies solely on the
language interface to support segmentation tasks. Our framework bridges the gap
between fine-grained perception and vision-language tasks, significantly
simplifying architectural design and training strategies while achieving
comparable or superior performance to methods with intricate task-specific
designs. After multi-task training on five standard visual perception datasets,
\ours outperforms the previous state-of-the-art generalist models by 12.3 mAP
on COCO instance segmentation and 3.3 mIoU on ADE20K semantic segmentation.
Furthermore, our method seamlessly integrates with existing MLLMs, effectively
combining fine-grained perception capabilities with their advanced language
abilities, thereby enabling more challenging tasks such as reasoning
segmentation. Code and models will be publicly available.