Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Abstract

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.

To appear in AAAI 2016

References in corpus (1)

• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

Cited by in corpus (5)

• Temporal Common Sense Acquisition with Minimal Supervision
• Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text
• KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning
• How Large Are Lions? Inducing Distributions over Quantitative Attributes
• Fit to Measure: Reasoning about Sizes for Robust Object Recognition