UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface

• URL: http://arxiv.org/abs/2503.01342v2
• Date: Tue, 04 Mar 2025 15:36:45 GMT
• Title: UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface
• Authors: Hao Tang, Chenwei Xie, Haiyang Wang, Xiaoyi Bao, Tingyu Weng, Pandeng Li, Yun Zheng, Liwei Wang,
• Abstract summary: ours is a framework that textbfUnifies textbfFine-grained visual perception tasks through an textbfOpen-ended language interface.<n>ours unifies object-level detection, pixel-level segmentation, and image-level vision-language tasks into a single model.<n>Our framework bridges the gap between fine-grained perception and vision-language tasks, significantly simplifying architectural design and training strategies.
• Score: 25.898592418636603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{U}nifies \textbf{F}ine-grained visual perception tasks through an \textbf{O}pen-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 are available at https://github.com/nnnth/UFO.

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