Towards Interpretable Controllability in Object-Centric Learning

Jinwoo Kim, Janghyuk Choi, Jaehyun Kang, Changyeon Lee, Ho-Jin Choi, Seon Joo Kim

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: computer vision; binding problem; object-centric learning; object representation; representation learning; human-interactive neural network;

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TL;DR: In this paper, we delve into the uncharted territory of interpretable controllability within object-centric learning, utilizing an unsupervised approach.

Abstract: The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities. Especially in the field of computer vision, object-centric learning (OCL) is extensively researched to better understand complex scenes by acquiring object representations or slots. While recent studies in OCL have made strides with complex images or videos, the interpretability and interactivity over object representation remain largely uncharted, still holding promise in the field of OCL. In this paper, we introduce a novel method, Slot Attention with Image Augmentation (SlotAug), to explore the possibility of learning interpretable controllability over slots in a self-supervised manner by utilizing an image augmentation strategy. We also devise the concept of sustainability in controllable slots by introducing iterative and reversible controls over slots with two proposed submethods: Auxiliary Identity Manipulation and Slot Consistency Loss. Extensive empirical studies and theoretical validation confirm the effectiveness of our approach, offering a novel capability for interpretable and sustainable control of object representations. Code will be available upon acceptance.

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Submission Number: 5088