Multi-Grained Cross-modal Alignment for Learning Open-vocabulary
  Semantic Segmentation from Text Supervision
Yajie Liu Pu Ge Qingjie Liu Di Huang
Recently, learning open-vocabulary semantic segmentation from text
supervision has achieved promising downstream performance. Nevertheless,
current approaches encounter an alignment granularity gap owing to the absence
of dense annotations, wherein they learn coarse image/region-text alignment
during training yet perform group/pixel-level predictions at inference. Such
discrepancy leads to suboptimal learning efficiency and inferior zero-shot
segmentation results. In this paper, we introduce a Multi-Grained Cross-modal
Alignment (MGCA) framework, which explicitly learns pixel-level alignment along
with object- and region-level alignment to bridge the granularity gap without
any dense annotations. Specifically, MGCA ingeniously constructs pseudo
multi-granular semantic correspondences upon image-text pairs and collaborates
with hard sampling strategies to facilitate fine-grained cross-modal
contrastive learning. Further, we point out the defects of existing group and
pixel prediction units in downstream segmentation and develop an adaptive
semantic unit which effectively mitigates their dilemmas including under- and
over-segmentation. Training solely on CC3M, our method achieves significant
advancements over state-of-the-art methods, demonstrating its effectiveness and
efficiency.