Collaborative annotation of semantic objects in images with multi-granularity supervisions

Per-pixel masks of semantic objects are very useful in many applications, which, however, are tedious to be annotated. In this paper, we propose a collaborative annotation approach to efficiently generate per-pixel masks of semantic objects in tagged images with multi-granularity supervisions. Given a set of tagged images, a computer agent is dynamically generated to roughly localize the semantic objects described by the tag. The agent first extracts massive object proposals and then infer the tag-related ones under the weak and strong supervisions from linguistically and visually similar images as well as previously annotated objects. By representing such supervisions by over-complete dictionaries, tag-related proposals can pop-out according to their sparse coding length, which are then converted to superpixels with binary labels. After that, human annotators participate in the annotation by flipping labels and dividing superpixels with clicks, which are used as click supervisions that teaches the agent to recover false positives/negatives in processing images with the same tags. Experimental results show that our approach can facilitate the annotation and generate object masks that are consistent with those generated by the LabelMe toolbox.