Conditional Object-Centric Learning from Video (2111.12594v2)

Abstract: Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets has shown that models with object-centric inductive biases can learn to segment and represent meaningful objects from the statistical structure of the data alone without the need for any supervision. However, such fully-unsupervised methods still fail to scale to diverse realistic data, despite the use of increasingly complex inductive biases such as priors for the size of objects or the 3D geometry of the scene. In this paper, we instead take a weakly-supervised approach and focus on how 1) using the temporal dynamics of video data in the form of optical flow and 2) conditioning the model on simple object location cues can be used to enable segmenting and tracking objects in significantly more realistic synthetic data. We introduce a sequential extension to Slot Attention which we train to predict optical flow for realistic looking synthetic scenes and show that conditioning the initial state of this model on a small set of hints, such as center of mass of objects in the first frame, is sufficient to significantly improve instance segmentation. These benefits generalize beyond the training distribution to novel objects, novel backgrounds, and to longer video sequences. We also find that such initial-state-conditioning can be used during inference as a flexible interface to query the model for specific objects or parts of objects, which could pave the way for a range of weakly-supervised approaches and allow more effective interaction with trained models.

• The paper introduces SAVi, a novel weakly-supervised model that leverages conditional cues to enhance video-based object segmentation and tracking.
• It employs temporal dynamics from optical flow and initial object localization to refine instance segmentation in synthetic datasets like MOVi and MOVi++, yielding robust FG-ARI scores.
• The results underscore SAVi’s potential for part-whole segmentation and its adaptability in complex visual scenarios, paving the way for practical AI applications.

An Overview of Conditional Object-Centric Learning from Video

The paper of object-centric representations has garnered significant attention due to their promising potential in enhancing systematic generalization within artificial intelligence. Object-centric approaches offer flexible abstractions favorable for constructing compositional models of the world, which are essential for high-level cognitive functions such as language processing, causal reasoning, and planning. This paper explores conditional object-centric learning from video data, aiming to improve multi-object segmentation and tracking in realistic synthetic environments.

Objectives and Methods

The research addresses inherent limitations in unsupervised object discovery methods, which typically fail to scale across complex textures and diverse datasets without priors. The authors shift toward a weakly-supervised paradigm, exploiting temporal dynamics from optical flow and object location cues to facilitate segmenting and tracking. They present SAVi (Slot Attention for Video), a sequential extension of Slot Attention, refined for video data, which predicts optical flow in synthetic scenes. Crucially, SAVi leverages initial-state-conditioning on simple hints, such as an object's center of mass from the first video frame. This conditioning aids in forming an efficient model for instance segmentation.

Findings and Results

Experimentation is conducted on both simplified and complex synthetic datasets, such as MOVi and MOVi++, which offer varying degrees of realism. SAVi, with conditional inputs, showcases superior segmentation capabilities compared to traditional propagation methods and even more advanced baselines such as SIMONe and SCALOR. The paper reports robust FG-ARI scores, reinforcing SAVi's ability to maintain temporal consistency and object identity in these environments.

The research further emphasizes future capabilities by demonstrating SAVi’s potential for part-whole segmentation. By modulating the granularity of the conditioning cues at the inference stage, SAVi can differentiate between tracking an entire composite object or its individual components. This adaptability suggests possible real-world applications where object granularity may not be predetermined during training.

Theoretical and Practical Implications

The findings advocate for incorporating weak supervision in training object-centric models, which could significantly reduce the gap between synthetic environments and real-world complexity. SAVi’s ability to generalize from synthetic data to static images and new object configurations indicates its applicability in more diverse scenarios beyond controlled synthetic environments, including potential integration into robotics and other video understanding domains.

Future Directions

Looking ahead, the paper underscores the necessity of overcoming challenges associated with optical flow supervision, which may not always be available or applicable in real-world scenarios. Exploring alternatives such as estimated flow could democratize SAVi’s deployment across diverse datasets. Enhancing the model’s adaptability to static object configurations and further testing its efficacy across varied dynamic scenes may also yield insights into practical deployment strategies.

The conditional strategies outlined could open avenues for semi-supervised learning architectures, allowing more interactive and efficient AI systems capable of aligning with the nuanced definitions of objects and tasks in real-world settings. Overall, this paper contributes valuable insights, showcasing how integrative approaches with minor supervision can pave the way for advanced AI applications characterized by high specificity and adaptability.