Cap2Seg: Inferring Semantic and Spatial Context from Captions for Zero-Shot Image Segmentation

Zero-shot image segmentation refers to the task of segmenting pixels from specific unseen semantic class. Previous methods mainly rely on historic segmentation tasks, such as using semantic embedding or word embedding of class names to infer a new segmentation model. In this work we describe Cap2Seg, a novel solution of zero-shot image segmentation that harnesses accompanying image captions for intelligently inferring spatial and semantic context for the zero-shot image segmentation task. As our main insight, image captions often implicitly entail the occurrence of a new class in an image and its most-confident spatial distribution. We define a contextual entailment question (CEQ) that tailors BERT-like text models. In specific, the proposed networks for inferring unseen classes consists of three branches (global / local / semi-global), which infer labels of unseen class from image level, image-stripe level or pixel level respectively. Comprehensive experiments and ablation studies are conducted on two image benchmarks, COCO-stuff and Pascal VOC. All clearly demonstrate the effectiveness of the proposed Cap2Seg, including a set of hardest unseen classes (i.e., image captions do not literally contain the class names and direct matching for inference fails).