Implicit Affordance Acquisition via Causal Action-Effect Modeling in the Video Domain

• URL: http://arxiv.org/abs/2312.11345v1
• Date: Mon, 18 Dec 2023 16:51:26 GMT
• Title: Implicit Affordance Acquisition via Causal Action-Effect Modeling in the Video Domain
• Authors: Hsiu-Yu Yang and Carina Silberer
• Abstract summary: Recent findings indicate that world knowledge emerges through large-scale self-supervised pretraining. We propose two novel pretraining tasks promoting the acquisition of two affordance properties in models. We empirically demonstrate the effectiveness of our proposed methods in learning affordance properties.
• Score: 5.188825486231326
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Affordance knowledge is a fundamental aspect of commonsense knowledge. Recent findings indicate that world knowledge emerges through large-scale self-supervised pretraining, motivating our exploration of acquiring affordance knowledge from the visual domain. To this end, we augment an existing instructional video resource to create the new Causal Action-Effect (CAE) dataset and design two novel pretraining tasks -- Masked Action Modeling (MAM) and Masked Effect Modeling (MEM) -- promoting the acquisition of two affordance properties in models: behavior and entity equivalence, respectively. We empirically demonstrate the effectiveness of our proposed methods in learning affordance properties. Furthermore, we show that a model pretrained on both tasks outperforms a strong image-based visual-linguistic foundation model (FLAVA) as well as pure linguistic models on a zero-shot physical reasoning probing task.

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