Abstract
Unsupervised object discovery is becoming an essential line of research for tackling recognition problems that require decomposing an image into entities, such as semantic segmentation and object detection. Recently, object-centric methods that leverage self-supervision have gained popularity, due to their simplicity and adaptability to different settings and conditions. However, those methods do not exploit effective techniques already employed in modern self-supervised approaches. In this work, we consider an object-centric approach in which DINO ViT features are reconstructed via a set of queried representations called slots. Based on that, we propose a masking scheme on input features that selectively disregards the background regions, inducing our model to focus more on salient objects during the reconstruction phase. Moreover, we extend the slot attention to a multi-query approach, allowing the model to learn multiple sets of slots, producing more stable masks. During training, these multiple sets of slots are learned independently while, at test time, these sets are merged through Hungarian matching to obtain the final slots. Our experimental results and ablations on the PASCAL-VOC 2012 dataset show the importance of each component and highlight how their combination consistently improves object localization. Our source code is available at: this https URL
Abstract (translated)
无监督的对象发现已经成为解决需要将图像分解成实体以解决识别问题的关键技术之一。最近，利用自监督的方法在对象中心方法中取得了广泛的应用，因为它们的简单性和对不同设置和条件的适应性。然而，这些方法并没有充分利用现代自监督方法中已经采用的有效技术。在本文中，我们考虑了一种以对象为中心的方法，其中通过查询表示的槽来重构DINO ViT特征。基于这种方法，我们提出了一个在输入特征上应用遮罩方案，该方案选择性地忽略了背景区域，使我们的模型在重构阶段更加关注显著的物体。此外，我们还将槽注意扩展到多查询方法，使模型能够学习多个设置，从而产生更稳定的掩码。在训练过程中，这些多个设置是独立学习的，而在测试时，这些设置通过匈牙利匹配合并为最终设置。我们对PASCAL-VOC 2012数据集的实验结果和分析表明了每个组件的重要性，并强调了它们组合一致地改善了物体定位。我们的源代码可在此处访问：https:// this URL
URL
https://arxiv.org/abs/2404.19654
