  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.
