Tuning-free Universally-Supervised Semantic Segmentation
<|reference_start|>Tuning-free Universally-Supervised Semantic Segmentation: This work presents a tuning-free semantic segmentation framework based on classifying SAM masks by CLIP, which is universally applicable to various types of supervision. Initially, we utilize CLIP's zero-shot classification ability to generate pseudo-labels or perform open-vocabulary segmentation. However, the misalignment between mask and CLIP text embeddings leads to suboptimal results. To address this issue, we propose discrimination-bias aligned CLIP to closely align mask and text embedding, offering an overhead-free performance gain. We then construct a global-local consistent classifier to classify SAM masks, which reveals the intrinsic structure of high-quality embeddings produced by DBA-CLIP and demonstrates robustness against noisy pseudo-labels. Extensive experiments validate the efficiency and effectiveness of our method, and we achieve state-of-the-art (SOTA) or competitive performance across various datasets and supervision types.<|reference_end|>
@article{yang2024tuning-free,
    title={Tuning-free Universally-Supervised Semantic Segmentation},
    author={Xiaobo Yang, Xiaojin Gong},
    journal={arXiv preprint arXiv:2405.14294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14294},
    primaryClass={cs.CV}
}