  Object proposal generation is an important and fundamental task in computer
vision. In this paper, we propose ProposalCLIP, a method towards unsupervised
open-category object proposal generation. Unlike previous works which require a
large number of bounding box annotations and/or can only generate proposals for
limited object categories, our ProposalCLIP is able to predict proposals for a
large variety of object categories without annotations, by exploiting CLIP
(contrastive language-image pre-training) cues. Firstly, we analyze CLIP for
unsupervised open-category proposal generation and design an objectness score
based on our empirical analysis on proposal selection. Secondly, a graph-based
merging module is proposed to solve the limitations of CLIP cues and merge
fragmented proposals. Finally, we present a proposal regression module that
extracts pseudo labels based on CLIP cues and trains a lightweight network to
further refine proposals. Extensive experiments on PASCAL VOC, COCO and Visual
Genome datasets show that our ProposalCLIP can better generate proposals than
previous state-of-the-art methods. Our ProposalCLIP also shows benefits for
downstream tasks, such as unsupervised object detection.
