Object-Centric Learning (OCL) seeks to enable Neural Networks to identify
individual objects in visual scenes, which is crucial for interpretable visual
comprehension and reasoning. Most existing OCL models adopt auto-encoding
structures and learn to decompose visual scenes through specially designed
inductive bias, which causes the model to miss small objects during
reconstruction. Reverse hierarchy theory proposes that human vision corrects
perception errors through a top-down visual pathway that returns to
bottom-level neurons and acquires more detailed information, inspired by which
we propose Reverse Hierarchy Guided Network (RHGNet) that introduces a top-down
pathway that works in different ways in the training and inference processes.
This pathway allows for guiding bottom-level features with top-level object
representations during training, as well as encompassing information from
bottom-level features into perception during inference. Our model achieves SOTA
performance on several commonly used datasets including CLEVR, CLEVRTex and
MOVi-C. We demonstrate with experiments that our method promotes the discovery
of small objects and also generalizes well on complex real-world scenes. Code
will be available at this https URL

提出了一种引入自顶向下路径的反层次指导网络（Reverse Hierarchy Guided Network），该网络在训练和推理过程中采用不同的方式，通过自顶向下的特征引导底层特征，同时将底层特征纳入感知过程，从而实现了性能优于其他模型的结果，并且在发现小物体和复杂场景中有良好的泛化能力。