Search for Concepts: Discovering Visual Concepts Using Direct Optimization
Pradyumna Reddy, Paul Guerrero, Niloy J. Mitra

TL;DR
This paper introduces a direct optimization approach for unsupervised image decomposition into objects, which improves generalization, accuracy, and data efficiency over traditional amortized inference methods.
Contribution
It presents a novel combination of gradient-based optimization and global search for object decomposition, demonstrating advantages over amortized inference.
Findings
More generalizable decomposition method
Fewer missed correct decompositions
Requires less training data
Abstract
Finding an unsupervised decomposition of an image into individual objects is a key step to leverage compositionality and to perform symbolic reasoning. Traditionally, this problem is solved using amortized inference, which does not generalize beyond the scope of the training data, may sometimes miss correct decompositions, and requires large amounts of training data. We propose finding a decomposition using direct, unamortized optimization, via a combination of a gradient-based optimization for differentiable object properties and global search for non-differentiable properties. We show that using direct optimization is more generalizable, misses fewer correct decompositions, and typically requires less data than methods based on amortized inference. This highlights a weakness of the current prevalent practice of using amortized inference that can potentially be improved by integrating…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
