Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Ali Farhadi; Hannaneh Hajishirzi; Hessam Bagherinezhad; Yejin Choi

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1602.00753 v1 pith:BVXJGXH4 submitted 2016-02-02 cs.AI cs.CV

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Hessam Bagherinezhad , Hannaneh Hajishirzi , Yejin Choi , Ali Farhadi This is my paper

classification cs.AI cs.CV

keywords sizeinformationsizesvisualhumanmethodobjectsreasoning

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original 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.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Language Is Not All You Need: Aligning Perception with Language Models
cs.CL 2023-02 conditional novelty 7.0

Kosmos-1 shows strong zero-shot and few-shot results on language tasks, image captioning, visual QA, OCR-free document understanding, and image recognition guided by text instructions.