Tuning-free Universally-Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2405.14294v1
• Date: Thu, 23 May 2024 08:13:52 GMT
• Title: Tuning-free Universally-Supervised Semantic Segmentation
• Authors: Xiaobo Yang, Xiaojin Gong,
• Abstract summary: This work presents a tuning-free semantic segmentation framework based on classifying SAM masks by CLIP. 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 to classify SAM masks, which reveals the intrinsic structure of high-quality embeddings.
• Score: 5.455525100072623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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