FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation (2303.17225v1)

Abstract: Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segmentation tasks. These customized design paradigms lead to fragmentation between various segmentation tasks, thus hindering the uniformity of segmentation models. Hence in this paper, we propose FreeSeg, a generic framework to accomplish Unified, Universal and Open-Vocabulary Image Segmentation. FreeSeg optimizes an all-in-one network via one-shot training and employs the same architecture and parameters to handle diverse segmentation tasks seamlessly in the inference procedure. Additionally, adaptive prompt learning facilitates the unified model to capture task-aware and category-sensitive concepts, improving model robustness in multi-task and varied scenarios. Extensive experimental results demonstrate that FreeSeg establishes new state-of-the-art results in performance and generalization on three segmentation tasks, which outperforms the best task-specific architectures by a large margin: 5.5% mIoU on semantic segmentation, 17.6% mAP on instance segmentation, 20.1% PQ on panoptic segmentation for the unseen class on COCO.

Overview of FreeSeg: A Universal Framework for Open-Vocabulary Image Segmentation

The paper presents FreeSeg, an innovative approach to unify, universalize, and open the scope of image segmentation, targeting a breadth of segmentation tasks without task-specific retraining. Developed as an all-in-one framework, FreeSeg addresses key limitations in existing segmentation methodologies by unifying semantic, instance, and panoptic segmentation through a single architecture and set of parameters, which is trained once.

Key Contributions and Methodology

FreeSeg combines a two-stage framework: the first stage generates universal mask proposals while the second leverages CLIP, a pre-trained model from text-image embeddings, to manage zero-shot classification tasks. The methodology encapsulates three primary contributions:

1. Unified and Universal Segmentation: FreeSeg's architecture consolidates multiple segmentation tasks into one seamless procedure, avoiding the pitfalls of task-specific designs like previous methodologies, such as that employed by ZSSeg. This single model handles diverse segmentation tasks through generalized network architecture, scoring superior performance across unseen classes.
2. Adaptive Prompt Learning: This is a critical addition to facilitate model robustness across tasks and scenarios. By embedding customizable prompts for task and category, FreeSeg can manage task-aware and context-sensitive concepts that drive improved accuracy and adaptability in zero-shot scenarios. The prompt learning is optimized during the training phase to integrate multi-task features into the text embeddings, leveraging the textual guidance for adaptive segmentation tasks.
3. Semantic Context Interaction and Test Time Prompt Tuning: The inclusion of semantic context interaction enhances the model's cross-modal alignment by allowing dynamic interaction between visual features and text prompts. During testing, the prompt tuning refines the adaptive class prompts ensuring higher prediction confidence through entropy optimization.

Experimental Results

FreeSeg demonstrated significant advancements in performance over existing state-of-the-art models across multiple datasets including COCO, ADE20K, and PASCAL VOC2012 in both seen and unseen segmentation tasks. For instance, FreeSeg achieved notable improvements with an additional 5.5% mIoU on unseen classes compared to ZSSeg on COCO for semantic segmentation, indicating its robustness and generalization capabilities.

In experimentations on instance and panoptic segmentation tasks, FreeSeg reached new benchmarks with better segmentation quality metrics than its predecessors, such as an improvement of 7.0% mAP for unseen classes over ZSI in COCO. Furthermore, the cross-dataset generalization test accentuated FreeSeg's robustness with superior transferability across different visual datasets.

Implications and Future Directions

This paper's implications are substantial, especially in the domain of open vocabulary and universal image segmentation. By reducing the need for task-oriented re-training, FreeSeg simplifies deployment strategies significantly in AI applications that require segmentation capabilities.

In looking ahead, this framework opens several avenues for future research, such as optimizing segmentation models for computational efficiency without sacrificing accuracy. Additionally, exploration of more complex image and semantic scenarios with FreeSeg's framework could further elevate its application in diverse visual environments while potentially reducing computational overhead. As AI continues to move towards more generalized and flexible models, frameworks like FreeSeg could be pivotal in shaping future developments.

In conclusion, FreeSeg represents a significant contribution to the segmentation field, redefining the approach to multi-tasking within a single framework, and enhancing the scope of open-vocabulary image segmentation without extensive re-training or resource-heavy modifications.