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

通过利用 DINO ViT 特征的集合查询表示来重建输入特征，我们提出了一种基于物体为中心的方法，通过遮罩输入特征，有选择性地忽略背景区域，以便模型在重建阶段更关注显著对象。此外，我们将 slot attention 拓展为多查询方法，使模型学习多套槽，产生更稳定的遮罩。我们在 PASCAL-VOC 2012 数据集上的实验结果和验证显示了每个组件的重要性，并突出了它们的组合如何不断改善物体定位。

掩码式多查询槽注意力用于无监督对象发现

Masked Multi-Query Slot Attention for Unsupervised Object Discovery

Object-centric methods have seen significant progress in unsupervised
decomposition of raw perception into rich object-like abstractions. However,
limited ability to ground object semantics of the real world into the learned
abstractions has hindered their adoption in downstream understanding
applications. We present the Neural Slot Interpreter (NSI) that learns to
ground and generate object semantics via slot representations. At the core of
NSI is an XML-like programming language that uses simple syntax rules to
organize the object semantics of a scene into object-centric program
primitives. Then, an alignment model learns to ground program primitives into
slots through a bi-level contrastive learning objective over a shared embedding
space. Finally, we formulate the NSI program generator model to use the dense
associations inferred from the alignment model to generate object-centric
programs from slots. Experiments on bi-modal retrieval tasks demonstrate the
efficacy of the learned alignments, surpassing set-matching-based predictors by
a significant margin. Moreover, learning the program generator from grounded
associations enhances the predictive power of slots. NSI generated programs
demonstrate improved performance of object-centric learners on property
prediction and object detection, and scale with real-world scene complexity.

通过利用类似 XML 的编程语言将场景的目标语义组织成面向对象的程序原语，神经插槽解释器 (Neural Slot Interpreter, NSI) 学习对目标语义进行细分和生成，通过共享嵌入空间上的双层对比学习目标原语，从而从地面关联模型推断的密集关联中生成面向对象的程序，提高对象中心化学习者在属性预测和对象检测方面的性能。