论文标题
提案：无监督的开放类别对象提案通过利用剪辑提示生成
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues
论文作者
论文摘要
对象提案生成是计算机视觉中的重要而基本的任务。在本文中，我们提出了建议，这是一种无监督的开放类别对象提案生成的方法。与以前需要大量边界框注释和/或只能为有限对象类别生成建议的作品不同，我们的提案Clip能够通过利用剪辑（对比性语言图像 - 预先培训）线索来预测各种对象类别的建议，而无需注释。首先，我们根据我们对建议选择的经验分析，分析了无监督的开放类别提案生成和设计目标得分的剪辑。其次，提出了一个基于图的合并模块来解决夹提示的局限性并合并了零碎的建议。最后，我们提出了一个建议回归模块，该模块根据剪辑提示提取伪标签，并训练轻量级网络以进一步完善建议。关于Pascal VOC，可可和视觉基因组数据集的广泛实验表明，与以前的最新方法相比，我们的提案CLIP可以更好地生成建议。我们的提案CLIP还显示了下游任务的好处，例如无监督的对象检测。
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.
