Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object detector. Many existing methods follow the standard multiple instance learning (MIL) paradigm and have achieved promising performance. However, the lack of deterministic information leads to part domination and missing instances. To address these issues, this paper focuses on identifying and fully exploiting the deterministic information in WSOD. We discover that negative instances (i.e. absolutely wrong instances), ignored in most of the previous studies, normally contain valuable deterministic information. Based on this observation, we here propose a negative deterministic information (NDI) based method for improving WSOD, namely NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and exploiting. In the collecting stage, we design several processes to identify and distill the NDI from negative instances online. In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively. Experimental results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO show that our method achieves satisfactory performance.

翻译：弱监督目标检测（WSOD）是一项具有挑战性的任务，其中仅使用图像级别标签（例如，整个图像中实例的类别）来训练目标检测器。许多现有方法遵循标准的多实例学习（MIL）范式，并取得了令人满意的性能。然而，确定性信息的缺失导致了局部主导和实例缺失问题。为解决这些问题，本文聚焦于识别并充分利用WSOD中的确定性信息。我们发现，多数先前研究中被忽略的负实例（即绝对错误的实例）通常包含有价值的确定性信息。基于此观察，本文提出一种基于负确定性信息（NDI）的方法来改进WSOD，即NDI-WSOD。具体而言，我们的方法包含两个阶段：NDI收集与利用。在收集阶段，我们设计了若干流程以在线识别并从负实例中提炼NDI。在利用阶段，我们将提取的NDI用于构建一种新颖的负对比学习机制和一种负引导实例选择策略，分别处理局部主导和实例缺失问题。在VOC 2007、VOC 2012和MS COCO等多个公开基准数据集上的实验结果表明，我们的方法取得了令人满意的性能。