UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface (2503.01342v2)

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.

• The paper introduces UFO, a unified framework that integrates fine-grained visual perception tasks like detection and segmentation into multimodal language models using an open-ended language interface, avoiding task-specific decoders.
• UFO innovatively reformulates segmentation as an embedding retrieval problem and employs upsampling with multiple mask tokens to enhance precision in segmentation tasks.
• Empirical results show UFO outperforms previous generalist models on COCO and ADE20K benchmarks, demonstrating significant improvements in performance while maintaining architectural simplicity.

Analyzing UFO: A Unified Framework for Fine-grained Visual Perception

The paper entitled "UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface" introduces a notable advancement in integrating fine-grained visual perception tasks, such as detection and segmentation, with multimodal LLMs (MLLMs) through a unified approach. The central contribution of this work is the UFO framework, which employs open-ended language interfaces to amalgamate object-level detection, pixel-level segmentation, and image-level vision-language tasks within a single model architecture. By avoiding the task-specific decoders often required in previous methodologies, UFO simplifies the architectural complexity and enhances training efficiency across various perception tasks.

Innovative Methodology

UFO innovatively transforms all task outputs into open-ended text sequences, a departure from traditional approaches that often employ complex task-specific designs and decoders, such as region proposal networks or mask decoders, which increase compatibility requirements and training intricacies. The key reinforcement in this paper is the novel reformulation of segmentation tasks as an embedding retrieval problem. This approach leverages the retrieval of mask token embeddings based on dot product similarity with image features, thereby generating high-similarity positions to produce segmentation masks. An additional enhancement is introduced by upsampling output masks using predicted multiple mask tokens, thereby improving mask precision and capturing minimal details in segmentation tasks.

Strong Numerical Results

The empirical validation of the UFO framework shows a substantial improvement in performance metrics across benchmark datasets. In the multi-task training context, UFO outperforms previous state-of-the-art generalist models, achieving a significant gain of 12.3 mAP on the COCO instance segmentation task and 3.3 mIoU on ADE20K semantic segmentation. Such results underline the efficacy of UFO in bridging fine-grained perception with MLLM capabilities. Moreover, the model showcases its versatility by successfully integrating with existing MLLMs, allowing it to handle complex tasks requiring both vision and language processing, such as reasoning segmentation.

Implications and Future Directions

The practical implications of the UFO framework are far-reaching. By providing a unified interface for diverse fine-grained perception tasks without reliance on complex task-specific parts, the approach offers a more scalable and adaptable solution for integrating perceptual capabilities into MLLMs. Theoretically, this framework proposes a paradigm where fine-grained perception tasks are effectively modeled within an open-ended language space, emphasizing the potential of language interfaces in vision tasks.

For future developments, the approach opens avenues for further enhancements in AI models that require tight integration of visual and linguistic understanding. It also suggests potential adaptations for tasks demanding more nuanced reasoning capabilities and comprehensive scene understanding, where fine-grained detail is crucial. As models continue to scale, maintaining efficiency while broadening task compatibility will remain a critical consideration, and approaches similar to UFO could significantly catalyze advancements in this arena.

The paper convincingly demonstrates the advantages of minimizing architectural and task-specific complexity in multi-task settings, setting a precedent for future exploration into unified multimodal models that can effortlessly incorporate detailed perceptual tasks into comprehensive AI systems.