  Recent approaches have shown that large-scale vision-language models such as
CLIP can improve semantic segmentation performance. These methods typically aim
for pixel-level vision-language alignment, but often rely on low resolution
image features from CLIP, resulting in class ambiguities along boundaries.
Moreover, the global scene representations in CLIP text embeddings do not
directly correlate with the local and detailed pixel-level features, making
meaningful alignment more difficult. To address these limitations, we introduce
MTA-CLIP, a novel framework employing mask-level vision-language alignment.
Specifically, we first propose Mask-Text Decoder that enhances the mask
representations using rich textual data with the CLIP language model.
Subsequently, it aligns mask representations with text embeddings using
Mask-to-Text Contrastive Learning. Furthermore, we introduce MaskText Prompt
Learning, utilizing multiple context-specific prompts for text embeddings to
capture diverse class representations across masks. Overall, MTA-CLIP achieves
state-of-the-art, surpassing prior works by an average of 2.8% and 1.3% on on
standard benchmark datasets, ADE20k and Cityscapes, respectively.
