Abstract
Unsupervised multi-object discovery (MOD) aims to detect and localize distinct object instances in visual scenes without any form of human supervision. Recent approaches leverage object-centric learning (OCL) and motion cues from video to identify individual objects. However, these approaches use supervision to generate pseudo labels to train the OCL model. We address this limitation with MR-DINOSAUR -- Motion-Refined DINOSAUR -- a minimalistic unsupervised approach that extends the self-supervised pre-trained OCL model, DINOSAUR, to the task of unsupervised multi-object discovery. We generate high-quality unsupervised pseudo labels by retrieving video frames without camera motion for which we perform motion segmentation of unsupervised optical flow. We refine DINOSAUR's slot representations using these pseudo labels and train a slot deactivation module to assign slots to foreground and background. Despite its conceptual simplicity, MR-DINOSAUR achieves strong multi-object discovery results on the TRI-PD and KITTI datasets, outperforming the previous state of the art despite being fully unsupervised.
Abstract (translated)
无监督多对象发现（MOD）的目标是在没有任何形式的人类监督的情况下，检测和定位视觉场景中的不同物体实例。最近的方法利用了以对象为中心的学习（OCL）以及视频中运动线索来识别单独的物体。然而，这些方法使用监督信息生成伪标签来训练OCL模型。我们通过MR-DINOSAUR——即“Motion-Refined DINOSAUR”——解决了这一限制，这是一种极简主义的无监督方法，它将自我监督预训练的OCL模型DINOSAUR扩展到无监督多对象发现任务上。 我们通过检索没有相机运动的视频帧来生成高质量的无监督伪标签，并在此基础上执行无监督光流的运动分割。使用这些伪标签细化了DINOSAUR的槽位表示，训练了一个槽位去激活模块，以将槽位分配给前景和背景对象。尽管概念上简单，MR-DINOSAUR在TRI-PD和KITTI数据集上的多对象发现结果中表现出了强大的性能，并且在完全无监督的情况下超过了之前的最佳水平。
URL
https://arxiv.org/abs/2509.02545
