Recent success of large-scale Contrastive Language-Image Pre-training (CLIP)
has led to great promise in zero-shot semantic segmentation by transferring
image-text aligned knowledge to pixel-level classification. However, existing
methods usually require an additional image encoder or retraining/tuning the
CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal
Transport (ZegOT) method that matches multiple text prompts with frozen image
embeddings through optimal transport. In particular, we introduce a novel
Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an
optimal mapping between multiple text prompts and visual feature maps of the
frozen image encoder hidden layers. This unique mapping method facilitates each
of the multiple text prompts to effectively focus on distinct visual semantic
attributes. Through extensive experiments on benchmark datasets, we show that
our method achieves the state-of-the-art (SOTA) performance over existing
Zero-shot Semantic Segmentation (ZS3) approaches.Comment: 18pages, 8 figure