Despite weakly supervised object detection (WSOD) being a promising step
toward evading strong instance-level annotations, its capability is confined to
closed-set categories within a single training dataset. In this paper, we
propose a novel weakly supervised open-vocabulary object detection framework,
namely WSOVOD, to extend traditional WSOD to detect novel concepts and utilize
diverse datasets with only image-level annotations. To achieve this, we explore
three vital strategies, including dataset-level feature adaptation, image-level
salient object localization, and region-level vision-language alignment. First,
we perform data-aware feature extraction to produce an input-conditional
coefficient, which is leveraged into dataset attribute prototypes to identify
dataset bias and help achieve cross-dataset generalization. Second, a
customized location-oriented weakly supervised region proposal network is
proposed to utilize high-level semantic layouts from the category-agnostic
segment anything model to distinguish object boundaries. Lastly, we introduce a
proposal-concept synchronized multiple-instance network, i.e., object mining
and refinement with visual-semantic alignment, to discover objects matched to
the text embeddings of concepts. Extensive experiments on Pascal VOC and MS
COCO demonstrate that the proposed WSOVOD achieves new state-of-the-art
compared with previous WSOD methods in both close-set object localization and
detection tasks. Meanwhile, WSOVOD enables cross-dataset and open-vocabulary
learning to achieve on-par or even better performance than well-established
fully-supervised open-vocabulary object detection (FSOVOD).

提出了一种弱监督开放式词汇目标检测（WSOVOD）框架，扩展了传统的弱监督目标检测方法，实现了对新概念的检测和仅使用图像级别标注的多样数据集的利用。