  Segmentation localizes objects in an image on a fine-grained per-pixel scale.
Segmentation benefits by humans-in-the-loop to provide additional input of
objects to segment using a combination of foreground or background clicks.
Tasks include photoediting or novel dataset annotation, where human annotators
leverage an existing segmentation model instead of drawing raw pixel level
annotations. We propose a new segmentation process, Text + Click segmentation,
where a model takes as input an image, a text phrase describing a class to
segment, and a single foreground click specifying the instance to segment.
Compared to previous approaches, we leverage open-vocabulary image-text models
to support a wide-range of text prompts. Conditioning segmentations on text
prompts improves the accuracy of segmentations on novel or unseen classes. We
demonstrate that the combination of a single user-specified foreground click
and a text prompt allows a model to better disambiguate overlapping or
co-occurring semantic categories, such as "tie", "suit", and "person". We study
these results across common segmentation datasets such as refCOCO, COCO, VOC,
and OpenImages. Source code available here.
