TAG: Guidance-free Open-Vocabulary Semantic Segmentation (2403.11197v1)

Abstract: Semantic segmentation is a crucial task in computer vision, where each pixel in an image is classified into a category. However, traditional methods face significant challenges, including the need for pixel-level annotations and extensive training. Furthermore, because supervised learning uses a limited set of predefined categories, models typically struggle with rare classes and cannot recognize new ones. Unsupervised and open-vocabulary segmentation, proposed to tackle these issues, faces challenges, including the inability to assign specific class labels to clusters and the necessity of user-provided text queries for guidance. In this context, we propose a novel approach, TAG which achieves Training, Annotation, and Guidance-free open-vocabulary semantic segmentation. TAG utilizes pre-trained models such as CLIP and DINO to segment images into meaningful categories without additional training or dense annotations. It retrieves class labels from an external database, providing flexibility to adapt to new scenarios. Our TAG achieves state-of-the-art results on PascalVOC, PascalContext and ADE20K for open-vocabulary segmentation without given class names, i.e. improvement of +15.3 mIoU on PascalVOC. All code and data will be released at https://github.com/Valkyrja3607/TAG.

• The paper proposes TAG, a novel method that performs open-vocabulary semantic segmentation without additional training, annotation, or guidance by leveraging pre-trained CLIP and DINO models.
• Its methodology computes precise segment candidates and retrieves class labels from an external database, eliminating the need for extensive pixel-level annotations.
• Experiments demonstrate a remarkable +15.3 mIoU improvement on PascalVOC, underscoring the approach’s superior performance compared to existing segmentation methods.

TAG: A Novel Approach to Open-Vocabulary Semantic Segmentation

Introduction

Semantic segmentation holds significant importance in the field of computer vision, facilitating the development of applications within robotics, medical imaging, and more by assigning class labels to each pixel in an image. Despite its fundamental role, traditional methods for semantic segmentation encounter major challenges, notably the requirement for pixel-level annotation and extensive training data, and the limitation of recognizing only a predefined set of classes. These limitations have paved the way for the exploration of unsupervised and open-vocabulary segmentation approaches. However, these methods either fail to accurately label the segmentation clusters or require explicit text queries for class guidance. Addressing these gaps, we explore a novel method, TAG (Training, Annotation, and Guidance-free), which leverages pre-trained models such as CLIP and DINO to perform open-vocabulary semantic segmentation without the need for additional training or detailed annotations. This approach facilitates the segmentation of images into meaningful categories using an external database for class retrieval.

TAG Framework

The TAG methodology consists of several key components:

1. Segment Candidates with DINO: Utilizes DINOv2-pretrained features to calculate segmentation candidates, focusing on achieving precise segmentation results without dense annotations.
2. Representative Segment Embeddings with CLIP: Employs per-pixel embedding features from a CLIP-pretrained model to create representative embeddings for each segment.
3. Segment Category Retrieval: Assigns class categories to segments by retrieving the closest matching sentence from an extensive external database, allowing for the inclusion of a wide array of categories without text guidance.

Our comprehensive experiments demonstrate TAG's effectiveness across various benchmarks. On the PascalVOC dataset, TAG achieved a notable improvement of +15.3 mIoU compared to existing methods, underlining its superior segmentation performance.

Technical Contributions

The paper's contributions can be distilled into three main points:

• Introduction of TAG: Presents a groundbreaking method for Training, Annotation, and Guidance-free open-vocabulary semantic segmentation that retrieves categories from an external database.
• Superior Segmentation Performance: Exhibits significant advancements over prior state-of-the-art techniques on benchmarks such as PascalVOC, demonstrating the efficacy of the proposed approach.
• Flexibility and Extensibility: The use of an external database for category retrieval not only facilitates flexibility in adapting to new scenarios but also allows easy incorporation of novel concepts without the need for model re-training.

Future Directions in AI

TAG represents a significant step towards overcoming the limitations that have long challenged traditional semantic segmentation methods. By eliminating the need for extensive supervision and predefined category sets, this approach opens up new possibilities for computer vision applications across various domains. Future developments could focus on enhancing the model's ability to segment and classify images with even higher granularity and accuracy, possibly by harnessing more advanced natural language processing techniques for more nuanced category differentiation. Furthermore, extending this framework to work seamlessly across different domains represents a valuable direction for research, potentially revolutionizing how machines interpret and understand complex visual data.

Conclusion

The TAG framework marks a notable advancement in the field of semantic segmentation, effectively addressing the critical challenges of training, annotation, and guidance constraints. Through its innovative use of pre-trained models and external databases for category retrieval, TAG showcases the potential for significant improvements in open-vocabulary segmentation tasks. As the demand for sophisticated computer vision applications continues to grow, such contributions are vital in pushing the boundaries of what is possible, paving the way for the next generation of AI-driven solutions.