Attend, Infer, Repeat: Fast Scene Understanding with Generative Models (1603.08575v3)

Abstract: We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.

• The paper introduces the AIR framework that iteratively attends to objects for unsupervised scene decomposition.
• It efficiently infers variable-dimensional latent spaces using a recurrent neural network integrated with generative models.
• Experimental results demonstrate competitive speed and accuracy in object counting and scene interpretation compared to supervised methods.

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

The paper "Attend, Infer, Repeat: Fast Scene Understanding with Generative Models" by Eslami et al. introduces the Attend-Infer-Repeat (AIR) framework, designed to efficiently perform inference in structured image models by explicitly reasoning about objects within scenes. Traditionally, structured models for image understanding were either too complex for tractable inference or insufficiently expressive. This research aims to bridge the gap by integrating deep networks with structured probabilistic models.

Framework Overview

AIR is a recurrent neural network framework that processes scene elements iteratively, attending to one object at a time. Crucially, it autonomously determines the number of inference steps required, allowing it to handle variable-dimensional latent spaces. The researchers employ a Bayesian perspective, utilizing a generative model to treat scene interpretation as inference, leveraging both structured and deep generative models like variational auto-encoders and probabilistic renderers.

The AIR architecture operates without supervision, successfully identifying multiple objects, determining their count, location, and class. This is achieved in a single forward pass, demonstrating speed and accuracy comparable to supervised approaches. Importantly, the framework supports both 2D scenes with learned distributed representations and 3D scenes using predefined renderers.

Contributions

1. Efficient Inference Scheme: The authors formalize a scheme for variable-dimensional latent space inference, treating it as an iterative process within a recurrent network.
2. Unsupervised Scene Decomposition: AIR dissects complex scenes into constituent objects in an unsupervised manner, proving advantageous over existing models such as DRAW, in terms of computational efficiency and generalization capability.
3. Generative Model Integration: The framework aligns high-dimensional representations with interpretable latent variables, enhancing learning efficiency and generalization.
4. Application to 3D Rendering: Demonstrating AIR's applicability, the authors utilize it to retrieve object counts, identities, and poses in intricate 3D scenes with occlusions, showcasing the potential of 'vision as inverse graphics' at unprecedented speeds.

Experimental Results

In experiments with datasets like multi-MNIST, overlapping sprites, and Omniglot glyphs, AIR achieved substantial inference accuracy. It demonstrated robust generalization, effectively handling more complex or unseen scenes. While AIR outperformed models like DRAW on interpolative tasks, both models showed limitations under certain extrapolation scenarios. Nonetheless, AIR maintained structural interpretability, making it highly effective for downstream tasks such as object counting and order determination.

Implications

The research presents a significant step in scalable and interpretative scene understanding. AIR's capacity for unsupervised learning and rapid inference positions it as a compelling tool for real-time applications in AI, robotics, and autonomous systems. Additionally, the plug-and-play nature of AIR facilitates integration with existing state-of-the-art components, broadening its utility across various domains.

Future Directions

Future explorations could include enhancing AIR's robustness to broader datasets, addressing gradient variance challenges, and refining the model structure to encompass wider natural variability. Additionally, transitioning AIR to semi-supervised scenarios could expand its applicability, offering nuanced scene interpretations in more complex environments.

In summary, the Attend-Infer-Repeat framework holds promise for advancing scene understanding in AI, providing efficient, robust, and interpretable solutions to longstanding inference challenges.