SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
This addresses the problem of scaling unsupervised object discovery to real-world multi-object videos for computer vision applications, representing an incremental advance over prior slot-based models.
The paper tackled the challenge of unsupervised object-centric learning in complex real-world videos by introducing SAVi++, a model that uses depth signals and scaling techniques to segment and track objects without supervision, achieving emergent segmentation on the Waymo Open dataset.
The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot-based models leveraging motion cues have recently shown great promise in learning to represent, segment, and track objects without direct supervision, but they still fail to scale to complex real-world multi-object videos. In an effort to bridge this gap, we take inspiration from human development and hypothesize that information about scene geometry in the form of depth signals can facilitate object-centric learning. We introduce SAVi++, an object-centric video model which is trained to predict depth signals from a slot-based video representation. By further leveraging best practices for model scaling, we are able to train SAVi++ to segment complex dynamic scenes recorded with moving cameras, containing both static and moving objects of diverse appearance on naturalistic backgrounds, without the need for segmentation supervision. Finally, we demonstrate that by using sparse depth signals obtained from LiDAR, SAVi++ is able to learn emergent object segmentation and tracking from videos in the real-world Waymo Open dataset.